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Applications of machine learning in animal behaviour studies
Open accessAbstract
In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them. These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods. The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data. In this review, we aim to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data. We start by describing the rationale behind ML and review a number of animal behaviour studies where ML has been successfully deployed. The ML framework is then introduced by presenting several unsupervised and supervised learning methods. Following this overview, we illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address. The first uses a large number of spectral and morphological characteristics that describe the appearance of pheasant, Phasianus colchicus, eggs to assign them to putative clutches. The second takes a continuous data stream of feeder visits from PIT (passive integrated transponder)-tagged jackdaws, Corvus monedula, and extracts foraging events from it, which permits the construction of social networks. Our final example uses aerial images to train a classifier that detects the presence of wildebeest, Connochaetes taurinus, to count individuals in a population. With the advent of cheaper sensing and tracking technologies an unprecedented amount of data on animal behaviour is becoming available. We believe that ML will play a central role in translating these data into scientific knowledge and become a useful addition to the animal behaviourist's analytical toolkit.
Checked 2026-10-09 · 2 references not checked yet
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review introduces machine learning to animal behaviourists and illustrates unsupervised and supervised workflows through three case studies: assigning pheasant eggs to clutches, extracting jackdaw foraging events to build social networks, and detecting wildebeest in aerial images. It argues ML is well suited to complex, high-dimensional behavioural data but is not a silver bullet and requires careful validation.
Key findings
- Pheasant eggs: 549 eggs from 30 females in 10 pens were described by 21 morphological and spectral measures; PCA retained four components explaining 96% of variance, and k-means/hierarchical clustering with silhouette width indicated k = 3 clutches.
- Jackdaws: around 1000 PIT-tagged individuals across three colonies were studied; an illustrative 4-week data set from 141 individuals at one feeder was modelled with a Gaussian mixture model and BIC, and the resulting network confirmed associations between mated adults, parents and offspring, and unrelated individuals.
- Wildebeest: 250 aerial images of 64 × 64 pixels (50 each for wildebeest, zebra, trees, rocks and grass) were classified using HOG features and random forests; 175 images were used for training and 75 for testing, and from over 1000 features only a couple of hundred were needed, though two wildebeest in the test set were misclassified as trees.
- The review states ML is hypothesis-free and prediction-focused, can handle nonlinearities and interactions, but can learn patterns different from human cues and is vulnerable to biased training data and mistaking correlation for causation.
Methods
- Narrative review plus three worked case studies, with documented code at https://github.com/jjvalletta/ML_Animal_Behaviour; methods covered include PCA, k-means, hierarchical clustering, Gaussian mixture models, decision trees, random forests, cross-validation and feature selection.
- Case-study data: pheasant eggs (30 females, 10 pens, 549 eggs, 21 morphological/spectral measures, reflectance spectra 300–700 nm); jackdaw PIT-tag feeder visits (around 1000 birds across three colonies; illustrative 4 weeks from 141 individuals at one feeder); wildebeest aerial photos (250 images, 64 × 64 pixels, five classes).
- Main analyses: PCA and clustering for pheasant eggs; GMM with BIC and adjacency-matrix construction for jackdaw social networks; HOG feature extraction, t-SNE visualization, random forests with variable importance and a confusion matrix for wildebeest detection.
Limitations
- The paper states ML is “no silver bullet”: models may use cues unlike human cues (e.g. wildebeest misclassified as trees), biased training data can cause erratic behaviour (the tank/no-tank anecdote), observational data can mistake correlation for causation, and reproducibility over independent data sets is the most rigorous validation.
- The paper states that determining the number of clusters k is a fundamental yet unsolved problem, is mathematically subjective, and the final decision rests with the practitioner.
- Evident from the text: the case studies are illustrative rather than fully validated—pheasant maternity validation by protein fingerprinting is proposed but not performed; the jackdaw analysis uses only 4 weeks, one feeder and 141 individuals; and the wildebeest classifier uses five classes for illustration although automated counting would require only two.
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Claims the paper makes
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The jackdaw social network we generated (see Fig. 4e) confirms the existence of foraging associations between mated adults and between parents and offspring.”
§ Applications of machine learning in animal behaviour studies > Cluster validity
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As depicted in Fig. 4b, the data are dominated by short intervisit periods corresponding to foraging events, interleaved with long periods of inactivity.”
§ Applications of machine learning in animal behaviour studies > Jackdaw Associations
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fig. 3c depicts the PCA outcome, where the first four components explained 96% of the variance in the data and were kept as features for subsequent analysis.”
- Also stated in: § CONCLUDING REMARKS · plain
“The first four components explained 96% of the variance in the data and were kept as features for subsequent analysis.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Also stated in: § CONCLUDING REMARKS · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“From an initial pool of over 1000 features only a couple of hundred predictors were needed to achieve good predictive performance on the training data set.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We could use the 'raw' data, that is, the individual pixel intensities; however, when we projected them onto the first two principal components for visualization (see Appendix Fig. A5a) no obvious groupings were present.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The outcome was similar to that from k-means, but using dendrograms, the results can be visualized irrespective of dimensionality (as opposed to k-means where results had to be projected onto the first two PCs), and also allows us to identify subgroups within each cluster.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A correlogram of all 21 morphological and spectral measures showed strong linear associations between some of the variables (see Fig. 3b).”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 29
- Probably not a substantial claim. 39
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When projected onto the first two PCs, the HOGs features showed promising grouping by class, although it was still somewhat concealed (see Appendix Fig. A5b).”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also reveals strong associations between unrelated individuals and shows how these associations relate to the spatial proximity between nestboxes.”
§ Applications of machine learning in animal behaviour studies > Cluster validity
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, the confusion matrix in Fig. 5e shows how two wildebeests in the test data were mistakenly labelled as trees.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Visual inspection of eggs suggested that withinindividual variation in egg morphology may be low while interindividual variation is high.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
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None of the sources cited for this claim have been checked yet.
In the paper
“The focus is on inference; estimating the parameters of the statistical model that most likely gave rise to the observed data, and providing uncertainty bounds for these estimates.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Statistical models start with an assumption about the underlying data distribution (e.g.Gaussian, Poisson).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This allows us to answer important questions across a range of topics, including movement ecology, social structure, collective behaviour, communication and welfare.”
§ Applications of machine learning in animal behaviour studies > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: In many areas of animal behaviour research, improvements in the ability to collect large and detailed data sets are outstripping the ability to analyse them.
- Rests on an assumption: Animal behaviour researchers are in a position where automatically collecting detailed data sets is becoming commonplace, but extracting knowledge from them is a daunting task, mainly due to the lack of accessible analytical tools.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data.”
§ Applications of machine learning in animal behaviour studies > Abstract
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: In many areas of animal behaviour research, improvements in the ability to collect large and detailed data sets are outstripping the ability to analyse them.
- Rests on an assumption: These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods.
- Rests on an assumption: Animal behaviour researchers are in a position where automatically collecting detailed data sets is becoming commonplace, but extracting knowledge from them is a daunting task, mainly due to the lack of accessible analytical tools.
None of the sources cited for this claim have been checked yet.
In the paper
“Both statistical modelling and ML seek to build a mathematical description, a model, of the data and the underlying mechanism it represents; thus inevitably there is substantial overlap between the two”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In animal behaviour, ML approaches can address otherwise intractable tasks, such as classifying species, individuals, vocalizations or behaviours within complex data sets.”
§ Applications of machine learning in animal behaviour studies > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: In many areas of animal behaviour research, improvements in the ability to collect large and detailed data sets are outstripping the ability to analyse them.
- Rests on an assumption: Animal behaviour researchers are in a position where automatically collecting detailed data sets is becoming commonplace, but extracting knowledge from them is a daunting task, mainly due to the lack of accessible analytical tools.
None of the sources cited for this claim have been checked yet.
In the paper
“For ML, the focus is typically on prediction; without necessarily assuming a functional distribution for the data, a model that achieves optimal predictive performance is identified.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“ML was used in a comparative assessment of welfare across multiple laboratory populations of mice (Chesler et al., 2002) permitting a wide range of potential explanatory factors, each with diverse distribution, to be considered simultaneously as well as the interactions between them.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Chesler et al., 2002 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this case, the individual pixel intensities are not explicitly related to the image's class, suggesting the need for hand-crafted features that describe the object in each image.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The OOB data are also used to compute an estimate of the importance of every predictor, which can be subsequently used for feature selection.”
§ Applications of machine learning in animal behaviour studies > Random Forests
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using such a classifier may soon enable the fully automated detection and identification of animals from aerial count images.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Machine learning can relieve this burden by detecting wildebeest in an image and therefore automating this aspect of the count.
None of the sources cited for this claim have been checked yet.
In the paper
“Random forests (Breiman, 2001a;Cutler et al., 2007) is an ensemble method developed to mitigate the problem of overfitting in decision trees.”
§ Applications of machine learning in animal behaviour studies > Random Forests
What the sources say
- Not checked yetcites Breiman, 2001a
- Not checked yetcites Cutler et al., 2007 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“While in traditional statistical modelling a hypothesis (model) is put forward and is then accepted/rejected depending on how consistent it is with the measured observations, ML methods learn this hypothesis directly from the training data set.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ML algorithms can deal with nonlinearities and interactions among variables because the models are flexible enough to fit the data (as opposed to rigid linear regression models, for example).”
§ Applications of machine learning in animal behaviour studies > SUPERVISED LEARNING
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Random forests can handle thousands of mixed categorical and continuous predictors and are robust to outliers; however, the interpretability of plain decision trees is lost.”
§ Applications of machine learning in animal behaviour studies > Random Forests
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, this flexibility needs to be constrained to avoid fitting noise (overfitting).”
§ Applications of machine learning in animal behaviour studies > SUPERVISED LEARNING
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“It is clear that ML can address different objectives in numerous distinct fields of animal behaviour and is thus becoming a staple approach for novel methods (Dankert, Wang, Hoopfer, Anderson, & Perona, 2009;Kabra et al., 2013).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Finer scale discriminations are possible at both the individual level (Cheng, Xie, Lin, & Ji, 2012) and the bird song elements level (Ranjard & Ross, 2008).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Cheng et al., 2012 source
- Not checked yetcites Ranjard & Ross, 2008
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Co-occurrences in time could be determined by ML clustering methods with individuals in the same foraging event (cluster) considered to have a social affiliation (Psorakis et al., 2015).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Psorakis et al., 2015 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Female pheasants may nest communally or lay eggs in multiple nests (Yom-Tov, 1980).”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
What the sources say
- Not checked yetcites Yom-Tov, 1980 source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“k-means clustering is popular because it is intuitive and computationally inexpensive.”
§ Applications of machine learning in animal behaviour studies > k-means
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ML is used to automate the classification of behaviours/activities (Kabra et al., 2013) and tracking movement trajectories (Dell et al., 2014).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is this hypothesis-free approach that makes ML an attractive choice for dealing with complex data sets.”
§ Applications of machine learning in animal behaviour studies > Abstract
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, they are prone to overfitting (small changes in the training data set may lead to significantly different trees) and their predictive performance can be poor compared to other algorithms.”
§ Applications of machine learning in animal behaviour studies > Decision Trees
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“If achieved, this will have major implications for conservation as currently the process of detecting and identifying animals within images is the main bottleneck for estimating population sizes from aerial surveys (Torney et al., 2016).”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
What the sources say
- Not checked yetcites Torney et al., 2016 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: Currently counting wildebeest from aerial photos is time consuming, as thousands of aerial photos need to be counted manually.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“GMMs, however, assume that data are generated from a mixture of multivariate Gaussians and lack a global optimum solution.”
§ Applications of machine learning in animal behaviour studies > Gaussian mixture model (GMM)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Determining the number of distinct clusters k in a data set is a fundamental yet unsolved problem.”
§ Applications of machine learning in animal behaviour studies > How many clusters?
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Such methods appear to be robust and capture real-life pair bonds well (Psorakis, Roberts, Rezek, & Sheldon, 2012).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Psorakis et al., 2012 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Decision trees are readily interpretable, directly used to generate rules, and computationally inexpensive to train, evaluate and store.”
§ Applications of machine learning in animal behaviour studies > Decision Trees
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The rationale behind dimensionality reduction is simple; although the collected data may seem high dimensional, the structure of the data can be represented by fewer variables.”
§ Applications of machine learning in animal behaviour studies > Dimensionality Reduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using unsupervised learning methods, we were able to reduce the dimensionality of the problem to a few biologically meaningful features, and subsequently assign eggs to females.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Without direct observation of laying or destructive genetic sampling from eggs, it is not possible to assign eggs to females.
In the paper
“Based on the work of Psorakis et al. (2012), Fig. 4a summarizes the workflow used to convert a long series of time stamps into a social network.”
§ Applications of machine learning in animal behaviour studies > Jackdaw Associations
What the sources say
- Not checked yetcites Psorakis et al. (2012) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They can handle both categorical and continuous data, and are fairly robust to outliers.”
§ Applications of machine learning in animal behaviour studies > Decision Trees
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, it is only applicable to continuous data where a mean is defined.”
§ Applications of machine learning in animal behaviour studies > k-means
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Feature extraction is a key step in ML; finding representations that are directly relevant to the task at hand (e.g.discriminating between two classes) will almost always result in better predictive accuracy than employing more complex models.”
§ Applications of machine learning in animal behaviour studies > Feature extraction
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There is also no guarantee of a global optimum solution; thus, the user is advised to start the algorithm at multiple distinct centroids.”
§ Applications of machine learning in animal behaviour studies > k-means
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ML can tackle a wide range of tasks, including classifying observations into predefined sets (Kabra, Robie, Rivera-Alba, Branson, & Branson, 2013), clustering data into groups that share an underlying process (Zhang, O'Reilly, Perry, Taylor, & Dennis, 2015) and regressing an outcome of interest against multiple factors and elucidating their contributory effect (Chesler, Wilson, Lariviere, Rodrig”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This knowledge can then be used to infer individual decision rules in collective motion (Katz, Tunstrom, Ioannou, Huepe, & Couzin, 2011;Nagy, Kos, Biro, & Vicsek, 2010) and to compute activity budgets for individuals without the need for continuous human observation or time-consuming video analysis.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“There are also cases where a mix of labelled and unlabelled data are available, which would require the use of a different type of ML methods: semisupervised learning (Chapelle, Sch€ olkopf, & Zien, 2006).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites Chapelle, Schölkopf, & Zien, 2006
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, as we have seen earlier, unsupervised learning methods are routinely used with labelled data during the data exploration stage.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To this end, automatically extracting predictive features from 'raw' data is the central theme of a new set of methods, in an active area of research, called Deep Learning (LeCun, Bengio, & Hinton, 2015).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites LeCun, Bengio, & Hinton, 2015 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“High-profile failures highlight how even experts in the field of ML can fall victim of common pitfalls in data-driven analytics.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The type of data (labelled or unlabelled) and the particular task at hand ultimately dictate the category of algorithms to choose from (see Table 1).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Instead of using hand-crafted features (as we did in the wildebeest identification problem), Deep Learning automatically discovers features amenable for prediction by recursively applying simple but nonlinear transformations to the data.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites LeCun, Bengio, & Hinton, 2015 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Associations between an outcome and a set of inputs could have occurred by chance, owing to a confounding factor or a biased data set.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites MacKinnon, Krull, & Lockwood, 2000 source
- Not checked yetcites Zuur, Ieno, & Elphick, 2010
- Not checked yetcites A. Liu & Ziebart, 2014
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reproducibility of a predictive model over multiple independent data sets over time is ultimately the most rigorous form of validation.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
Strength:boosted· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“These methods make use of unlabelled data to improve the performance of predictive models identified from labelled observations.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites Chapelle, Schölkopf, & Zien, 2006
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, there are no hard and fast rules to this process and it has been shown that the performance of several state-of-the-art supervised learning algorithms are similar (Fern andez-Delgado, Cernadas, Barro, & Amorim, 2014).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites Fernandez-Delgado, Cernadas, Barro, & Amorim, 2014
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
In many areas of animal behaviour research, improvements in the ability to collect large and detailed data sets are outstripping the ability to analyse them.
“In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them.”
The paper relies on this without support.
These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods.
“These diverse, complex and often highdimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods.”
The paper relies on this without support.
Animal behaviour researchers are in a position where automatically collecting detailed data sets is becoming commonplace, but extracting knowledge from them is a daunting task, mainly due to the lack of accessible analytical tools.
“Animal behaviour researchers are thus in a position where automatically collecting detailed data sets is becoming commonplace, but extracting knowledge from them is a daunting task, mainly due to the lack of accessible analytical tools.”
The paper relies on this without support.
Without direct observation of laying or destructive genetic sampling from eggs, it is not possible to assign eggs to females.
“Without direct observation of laying or destructive genetic sampling from eggs, it is not possible to assign eggs to females.”
The paper relies on this without support.
Uncovering social structures in jackdaws allows us to infer individualized social relationships and determine how novel information spreads through social groups.
“Uncovering social structures in jackdaws allows us to infer individualized social relationships and determine how novel information spreads through social groups.”
The paper relies on this without support.
It is unclear how one specifies the length of foraging events without fixing an arbitrary time window.
“It is unclear, however, how one specifies the length of these foraging events without fixing an arbitrary time window.”
The paper relies on this without support.
Currently counting wildebeest from aerial photos is time consuming, as thousands of aerial photos need to be counted manually.
“Currently this process is time consuming, as thousands of aerial photos need to be counted manually.”
The paper relies on this without support.
Machine learning can relieve this burden by detecting wildebeest in an image and therefore automating this aspect of the count.
“Machine learning can relieve this burden by detecting wildebeest in an image and therefore automating this aspect of the count.”
The paper relies on this without support.
Research on jackdaw social structure has largely been confined to captive studies and there is limited information on patterns of association in natural populations.
“However, research has largely been confined to captive studies and there is limited information on patterns of association in natural populations.”
The paper relies on this without support.
HOGs features are hard to interpret and are likely to exhibit nonlinear interactions among them.
“HOGs features are hard to interpret and are likely to exhibit nonlinear interactions among them (a stark contrast to the pheasant eggs data where PCA was justified by the linear relationships between variables of the same theme).”
The paper relies on this without support.
Biased training data sets cause erratic behaviour of the predictive model.
“For example, biased training data sets cause erratic behaviour of the predictive model.”
The paper relies on this without support.
Features that are directly relevant to the outcome being predicted tend to make the predictive performance insensitive to the choice of algorithm.
“Features that are directly relevant to the outcome being predicted tend to make the predictive performance insensitive to the choice of algorithm.”
The paper relies on this without support.
ML offers a hypothesis-free approach to model complex data sets where the type of relationship between measured variables is unknown.
“ML offers a hypothesis-free approach to model complex data sets where the type of relationship between measured variables is unknown.”
The paper relies on this without support.
ML algorithms mine for patterns in the data that are best at predicting an outcome.
“ML algorithms mine for patterns in the data that are best at predicting an outcome.”
The paper relies on this without support.
Typically, ML algorithms mine for patterns in observational data, rather than experimental data, where correlation can be mistaken for causation.
“Typically, ML algorithms mine for patterns in observational data, rather than experimental data, where correlation can be mistaken for causation.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Opposite objectives stated for separation between clusters
MinorThe clustering section says to maximize similarity between clusters, while Figure A1 says to maximize the distance between clusters. Those are opposite objectives.
“each cluster should be sufficiently different (maximize intercluster similarity; see Appendix Fig. A1).”
“maximize the distance between distinct clusters (intercluster distance).”
Opposite objectives stated for similarity within clusters
MinorThe clustering section says observations within a group should be similar, but Figure A1 says to minimize within-group similarity.
“The data in each group should be similar (minimize intracluster distance)”
“to minimize the within-group similarity (intracluster distance)”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · cited 2×
“Both statistical modelling and ML seek to build a mathematical description, a model, of the data and the underlying mechanism it represents; thus inevitably there is substantial overlap between the two (Breiman, 2001b; Friedman, 2001; Zoubin Ghahramani, 2015).”
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · cited 2×
“The caret package in R (Kuhn, 2008) and scikit-learn module in Python (Pedregosa et al., 2011) provide a common interface to a wide range of ML algorithms, together with numerous auxiliary functions to perform visualization, cross-validation, model evaluation and more.”
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · cited 1×
“Examples of these techniques include LASSO (Tibshirani, 1996), where within a linear regression framework coefficients of poor predictors are ‘shrunk’ to zero.”
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · cited 1×
“Such higher-order structures were extracted using rotation-invariant histograms of oriented gradients (HOGs; Dalal & Triggs, 2005; Liu et al., 2014; Torney et al., 2016).”
(2008) · Journal of Machine Learning Research · Cited by 33,051
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392 · cited 2×
“In the section Case Studies, we present two approaches to estimating k, using silhouette width (Rousseeuw, 1987) and the Bayesian information criterion (BIC).”
Flagged first, then by citations
5 of the 104 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Bishop, 2006, Vol. 4
- Broom, 1993, Vol. 30
- Hastie, 2009, Vol. 1
Cited by
Sorted by citations
Items 501–504 of 504
Dursun, C., Gül, S., & Özdemır, N. (2026) · Journal of Morphology · doi:10.1002/jmor.70162
“They frequently achieve high classification accuracy in situations where conventional statistical approaches are limited (Valletta et al. 2017; Lürig et al. 2021; Sharma et al. 2023; de Lima et al. 2024; Zhang et al. 2024).”
Borba, J. V., Müller, M. L., Fontana, B. D., Parker, M. O., & Rosemberg, D. B. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.09.021
Torres, A., & Fernandez, M. (2024) · Journal of Computational and Data-Driven Materials Engineering · doi:10.68159/z116548786
Babatounde, C., Poggi, B., Vittori, E., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39953-3_11
Metadata
- Authors
- Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J.
- Affiliations
- University of Exeter (GB)
- Corresponding author
- Valletta, John Joseph
- Venue
- Animal Behaviour
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 124
- Pages
- 203-220
- ISSN
- 0003-3472 (ISSN-L)
- OpenAlex
- W2581974582
- Cited by
- 505
- References
- 104
- Citations by year
- 2026: 29 · 2025: 57 · 2024: 68 · 2023: 70 · 2022: 84 · 2021: 69 · 2020: 47 · 2019: 48 · 2018: 27 · 2017: 5
- FWCI
- 26.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Animal Behaviour (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- Open Research Exeter (University of Exeter) (acceptedVersion, cc-by)
- Figshare (submittedVersion)
- Funders
- MRC (MR/M003906/1)
- BBSRC David Phillips Fellowship (BB/H021817/1)
- BBSRC SWDTP studentship (630051486)
- ERC consolidator award (616474)
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- machine learning, behavioural data analysis, unsupervised learning, supervised learning, jackdaw, social networks, aerial imagery, population counting, passive integrated transponders
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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