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Maximum entropy modeling of species geographic distributions
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AUC is an effective threshold-independent measure that allows comparison of modeling approaches.
“AUC is an effective threshold-independent measure that allows us to compare modeling approaches [17].”
From Advancing predictive modeling in archaeology: an evaluation of regression and machine learning methods on the grand staircase-escalante national monument (2020) · cites this paper as [17]
Response functions are formed by weighting predictor values where sites occur by how common that value is across the entire study area, irrespective of presences or absences.
“Response functions are formed by weighting predictor values where sites occur by how common that value is across the entire study area, irrespective of presences or absences [17].”
From Advancing predictive modeling in archaeology: an evaluation of regression and machine learning methods on the grand staircase-escalante national monument (2020) · cites this paper as [17]
Model results were evaluated using a threshold-independent measure, the area under the Receiver Operating Characteristic curve (AUC).
“We evaluate model results using a threshold-independent measure, the area under the Receiver Operating Characteristic curve (AUC) [17,91].”
From Advancing predictive modeling in archaeology: an evaluation of regression and machine learning methods on the grand staircase-escalante national monument (2020) · cites this paper as [17]
MAXENT commonly outperformed other modelling techniques that use presence-only or presence-absence data, especially when dealing with small sample sizes or non-random sampling.
“MAXENT v. 3.3.3 (http://www.cs.princeton.edu/~schapire/ maxent/) was used to model terra preta distributions, as it commonly outperformed other modelling techniques that use presence-only or presence-absence data, especially when dealing with small sample sizes or non-random sampling [47-49].”
From Predicting pre-columbian anthropogenic soils in amazonia (2014) · cites this paper as [49]
A small regularization multiplier will result in a more localized output.
“A small regularization multiplier will result in a more localized output (Phillips et al., 2006; Phillips & Dudík 2008).”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Phillips et al., 2006
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Metadata
- Authors
- Phillips, S. J., Anderson, R. P., & Schapire, R. E.
- Venue
- Ecological Modelling
- Publisher
- Elsevier BV
- Published
- 2006-01
- Volume
- 190
- Issue
- 3-4
- Pages
- 231-259
- ISSN
- 0304-3800
- Cited by
- 15,396
- References
- 5
- Language
- en
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.
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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