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Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification
Open accessAbstract
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database.
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“More advanced face frontalization involving the approximation of 3D shape is useful when dealing with 3D head pose variation [104], [105].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [105]
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313
Flagged first, then by citations
Research works printed but not linked: 1
- Ekman, 2002, Facial Action Coding System—The Manual on CD-ROM
Cited by
Sorted by citations
Items 201–297 of 297
Mao, S., Li, A., Luo, Y., Gou, S., Qi, M., Li, T., Wei, X., Su, B., & Gu, N. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3533308 · Cited by 4
Tsai, F.-S., Weng, Y.-M., Ng, C.-J., & Lee, C.-C. (2019) · 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023134 · Cited by 4
Head Movement Dynamics based Pain Detection using Spatio-Temporal Network
Semwal, A., & Londhe, N. D. (2021) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin52536.2021.9566047 · Cited by 3
Children’s Pain Identification Based on Skin Potential Signal
Li, Y., He, J., Fu, C., Jiang, K., Cao, J., Wei, B., Wang, X., Luo, J., Xu, W., & Zhu, J. (2023) · Sensors · doi:10.3390/s23156815 · Cited by 4
Shaping the Video Conferences of Tomorrow With AI
Mendes, P. R. C., Vieira, E. S., de Freitas, P. V. A., Busson, A. J. G., Guedes, Á. L. V., Neto, C. D. S. S., & Colcher, S. (2020) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia_estendido.2020.13082 · Cited by 4
A Novel Convolutional Neural Network for Facial Expression Recognition
Li, J., Mi, Y., Yu, J., & Ju, Z. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-7986-4_28 · Cited by 4
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition in the WildPreprint
Liu, P., Lin, Y., Meng, Z., Deng, W., Zhou, J. T., & Yang, Y. (2020) · arXiv (Cornell University) · Cited by 4
Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable AlgorithmsPreprint
Ye, Q., & Xie, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12356 · Cited by 4
Sri, A. N. S., Nithin, S., Vasist, P., Mundra, V., Babu, R. V., & Anupama, Y. K. (2023) · International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS) · doi:10.1109/icaecis58353.2023.10170170 · Cited by 3
Tang, X., Hu, P., Yu, D., Yu, K., & Xia, D. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8148 · Cited by 3
A Multi-Modal Multi-Expert Framework for Pain Assessment in Postoperative Children
Liang, Z., Luo, H., Chen, X., Zhong, Z., Fan, C., Song, X., Li, B., & Lv, J. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3567307 · Cited by 3
A Novel Approach for Pain Intensity Detection by KAZE Features
Vaish, A., & Gupta, S. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-7091-5_1 · Cited by 3
Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units
Zhang, Y., Fan, Y., Dong, W., Hu, B.-G., & Ji, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2947201 · Cited by 3
Facial Emotion Recognition in Static and Live Streaming Image Dataset Using CNN
Seal, A., Saha, R., Kumar, R., Goenka, S., & Dey, L. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-10766-5_23 · Cited by 3
Edge Artificial Intelligence-Based Facial Pain Recognition During Myocardial Infarction
M, M. H., C, S. K. H., Mallikarjun, S., & Prasad, A. (2023) · Journal of Automation Mobile Robotics & Intelligent Systems · doi:10.14313/jamris/3-2022/23 · Cited by 4
Pain Detection From Facial Expressions
Aboussalim, A., Idrissi, N., & Chakib, H. (2024) · International Symposium on Signal, Image, Video and Communications (ISIVC) · doi:10.1109/isivc61350.2024.10577824 · Cited by 3
Facial Expression Monitoring via Fine-Grained Vision-Language Alignment
Ren, W., Gao, Y. Q., Chen, X., Han, Z. J., Wang, Z., Wang, J., & Liu, H. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3464519 · Cited by 3
Ilyas, C. M. A., Haque, M. A., Rehm, M., Nasrollahi, K., & Moeslund, T. B. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-26756-8_18 · Cited by 3
Self-citationPain detection from facial landmarks using spatial-temporal deep neural network
Choo, K. W., & Du, T. (2021) · International Conference on Digital Image Processing · doi:10.1117/12.2601059 · Cited by 3
Yang, Y., Cheng, W.-H., Lin, E.-T., Liu, A.-S., Ko, C., Huang, C., Tsai, C., & Fu, L. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01534-2 · Cited by 2
Facial Expressions as a Nexus for Health Assessment
Sooriyaarachchi, J., & Jiang, D. (2026) · Bioengineering · doi:10.3390/bioengineering13020208 · Cited by 2
Graph convolutional networks for pain detection via telehealth
Anwar, S., Milanova, M. G., Adbulla, S., & Muhammed, S. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90531-2.00006-0 · Cited by 2
Artificial Intelligence in Pain Management
Fan, B. (2023) · Springer eBooks · doi:10.1007/978-981-99-5925-9_9 · Cited by 2
Human Affect Recognition Using Deep Learning Approaches
Seshaiah, M., Bhargavi, D., Hema, H. P., Mallikashree, N., & Manuja, C. R. (2024) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs61492.2024.10616539 · Cited by 2
Rau, M., & Ertuğrul, I. Ö. (2024) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw63320.2024.00008 · Cited by 2
Multimodality Pain and related Behaviors Recognition based on Attention Learning
Huynh, V. T., Yang, H.-J., Lee, G.-S., & Kim, S.-H. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00034 · Cited by 2
Comparison of Convolutional Neural Network Performances in Pain Facial Expression Recognition
Lima, L., Santos, P., Ribeiro, B., Magalhães, W., Serrão, M. K. M., Costa, M. G. F., & Filho, C. F. F. C. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim56729.2023.10373515 · Cited by 2
A New Deep Neural Network Based Learning to Rank Method for Information Retrieval
Fu, M., Qu, H., Li, F., & Liu, Y. (2018) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2018.8812518 · Cited by 2
Multi-modal Analysis for the Automatic Evaluation of Epilepsy
Ahmedt‐Aristizabal, D. (2019) · Queensland University of Technology · doi:10.5204/thesis.eprints.132537 · Cited by 2
Praveen, R. G., Granger, É., & Cardinal, P. (2020) · arXiv (Cornell University) · Cited by 2
How Can AI Recognize Pain and Express EmpathyPreprint
Cao, S., Fu, D., Yang, X., Barros, P. V. A., Wermter, S., Liu, X., & Wu, H. (2021) · arXiv.org · doi:10.48550/arxiv.2110.04249 · Cited by 1
Face-tracking algorithm for large-amplitude head motions with a 7-DOF manipulator
Zhang, S., Zhao, C., Yuan, X., Ouyang, B., & Yang, S. (2023) · Robotica · doi:10.1017/s0263574723000991 · Cited by 1
Automated pain detection using STA-LSTM on facial landmarks
Du, T., Choo, K. W., Kong, W. M., Tan, C. W., Teo, J. C., Chan, D. X. H., & Sng, B. L. (2024) · International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI) · doi:10.1145/3665065.3665072 · Cited by 1
Pain Recognition Algorithm Based on Improved Xception and Attention Mechanism
Geng, Y., Lu, S., Guo, S., & Gu, B. (2024) · International Symposium on Computer Science and Intelligent Control (ISCSIC) · doi:10.1109/iscsic64297.2024.00051 · Cited by 1
AI-Based Noninvasive Pain Detection in Alzheimer’s Patients Using Facial Micro-expression Analysis
Brown, E., Lu, A. Y., & Lu, W. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87769-8_10 · Cited by 1
Enhancing pain intensity evaluation via an attention-driven channel–spatial fusion network
Gao, F., Qing, L., Li, L., Yang, G., Xu, R., & Gao, L. (2025) · The Visual Computer · doi:10.1007/s00371-025-04167-2 · Cited by 1
Adaptive GCRA-CNN-LSTM: An Enhanced Model for Stock Price Prediction
Xu, X., & Du, Y. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228892 · Cited by 1
Amer, M., Kassab, M. I., & Alshurman, W. (2026) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-026-01720-8 · Cited by 1
Lu, Z., Jasin, A. M., Zulkifle, F. A., Shamsuddin, M. R., Ul-Saufie, A. Z., Fu, M., & Zhao, J. (2026) · Journal of Advanced Transportation · doi:10.1155/atr/2975355 · Cited by 1
Slakkham, B., & Bhattarakosol, P. (2018) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2018.8650336 · Cited by 1
Learning to focus on region-of-interests for pain intensity estimation
Vu, M. T., & Beurton‐Aimar, M. (2023) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg57933.2023.10042583 · Cited by 1
Artificial Intelligence: An Overview
Jiang, H. (2023) · Springer eBooks · doi:10.1007/978-981-99-5925-9_1 · Cited by 1
Feline SentiNet: Deciphering Cat Discomfort with Deep Learning and Random Forest for Pain Grading
Suryavanshi, A., Jain, V. K., Kukreja, V., Choudhary, A., & Chamoli, S. (2023) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses60034.2023.10465552 · Cited by 1
Learning Rate Analysis for Pain Recognition Through Viola-Jones and Deep Learning Methods
Islamadina, R., Saddami, K., Arnia, F., Abidin, T. F., Muharar, R., Irwandi, M., & Aziz, A. S. (2024) · Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) · doi:10.22146/jnteti.v13i2.9466 · Cited by 1
Chronic Pain Estimation Through Deep Facial Descriptors Analysis
Mauricio, A., Peña, J., Dianderas, E., Mauricio, L., Díaz, J., & Morán, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-46140-9_17 · Cited by 1
FERCaps: A Capsule-Based Method for Face Expression Recognition from Frontal Face Images
Hu, Q., Shu, Q., Bai, M., Yao, X., & Shu, K. (2019) · DEStech Transactions on Environment Energy and Earth Science · doi:10.12783/dteees/peems2019/34027 · Cited by 1
Commentary: The Dynamic Features of Lip Corners in Genuine and Posed Smiles
Li, Y., Shi, Z., Zhang, H., Luo, L., & Fan, G. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01610 · Cited by 1
Avaliação de dor em expressão facial neonatal por meio de redes neurais profundas
Buzuti, L. F. (2020) · doi:10.31414/ee.2020.d.131151 · Cited by 1
Automatic Pain Assessment Through Facial Expressions
Seladji, I. (2020) · RIT Scholar Works (Rochester Institute of Technology) · Cited by 1
Alkhouli, M., Al‐Nerabieah, Z., Alissa, S. I., & Dashash, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4626256 · Cited by 1
Facial Analysis Models for Face and Facial Expression Recognition
Munasinghe, S. (1905) · Queensland University of Technology · doi:10.5204/thesis.eprints.118197
Deep DA for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labeled VideosPreprint
Praveen, R. G., Granger, É., & Cardinal, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.15675
Rezaei, S., Moturu, A., Zhao, S., Prkachin, K. M., Hadjistavropo, T., & Taati, B. (2020) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3395035.3425353
Self-Supervised GAN For Occluded Facial Expression Recognition
Liao, J., & Wang, X. (2021) · International Conference on Neuromorphic Computing (ICNC) · doi:10.1109/icnc52316.2021.9609002
Ubiquitous Technologies for Emotion Recognition
Baños, O., Castro, L. A., & Villalonga, C. (2021) · MDPI eBooks · doi:10.3390/books978-3-0365-1801-5
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Verma, M., Vipparthi, S. K., & Singh, G. (2022) · doi:10.36227/techrxiv.20085557.v1
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Verma, M., Vipparthi, S. K., & Singh, G. (2022) · doi:10.36227/techrxiv.20085557
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Hartini, L., Sudana, D., & Gunawan, W. (2023) · Theory and Practice in Language Studies · doi:10.17507/tpls.1308.17
Facial Emotion Recognition using Deep Learning: Advancements, Challenges, and Future DirectionsPreprint
Kaur, N., & Kaur, K. (2023) · Research Square · doi:10.21203/rs.3.rs-3244446/v1
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Gao, F. Q., Qing, L., Li, L., Xiong, K., Xu, R., & Gao, L. (2024) · Research Square · doi:10.21203/rs.3.rs-4193042/v1
Alkhouli, M., Al‐Nerabieah, Z., Alissa, S. I., & Dashash, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4246575/v1
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Sinha, P. K., Bhandari, P., Sharma, A. K., Arora, Y., & Kumar, A. (2024) · International Conference on Electrical Electronics and Computing Technologies (ICEECT) · doi:10.1109/iceect61758.2024.10739027
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Katranzopoulou, M., Delibasis, K. K., & Maglogiannis, I. G. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-88486-3_8
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Özdemir, C. (2025) · Türkiye teknoloji ve uygulamalı bilimler dergisi. · doi:10.70562/tubid.1736747
Joice, C. S., Shanthini, S. H., & Jenisha, C. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06253-6_30
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Lu, Z., Zulkifle, F. A., Japeri, A. Z. U.-S. M., Shamsuddin, M. R., & Jasin, A. M. (2025) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.130422
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Li, D., Liang, Y., Qian, S., Sun, Y., Wu, H., Jia, W., Han, W., & Fu, Y. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114705
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Towards Intelligent Pain Monitoring Systems: A Survey of Recent Technologies and Methods
Naseer, A., Tayyib, N., & Rashid, S. (2026) · Sensors · doi:10.3390/s26082447
Uejima, K., Takahashi, T., Matsui, M., Fukushima, S., & Nishi, Y. (2026) · Cureus · doi:10.7759/cureus.107883
Cross-spectral fusion of thermal and RGB imaging for objective pain estimation
Othmani, O. E., & Naouali, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001424
Liu, X., Li, H., Zhou, T., Yang, Z., Deng, T., & Xiao, H. (2025) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait67930.2025.11521945
Bi, N., Wang, T., Yin, L., & Sharma, V. (2026) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg67764.2026.11556981
Zero-shot multimodal pain estimation via synthetic pain simulation and domain-invariant learning
Othmani, O. E., & Naouali, S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1827727
Pain-Adapter: A Parameter-Efficient Adaptation Network for Facial Video-based Pain Assessment
Hu, X., Wang, L., Wang, Z., & Li, H. (2026) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3809986.3810091
Re-Evaluating Single-Backbone Transfer Learning for Pain Assessment from Facial Expressions
AL-Edwan, R., & Jafar, I. (2026) · IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies · doi:10.1109/aeect69724.2026.11657863
Metadata
- Authors
- Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X.
- Affiliations
- Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain
- Universitat Autònoma de Barcelona (ES)
- Aalborg University (DK)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2022-05
- Volume
- 52
- Issue
- 5
- Pages
- 3314-3324
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2587128043
- Cited by
- 297
- References
- 69
- Citations by year
- 2026: 12 · 2025: 28 · 2024: 33 · 2023: 32 · 2022: 26
- FWCI
- 43.48
- Type
- journal article
- Language
- en
- Open access
- green
- vbn.aau.dk
- Licence
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (acceptedVersion, other-oa)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, public-domain)
- Funders
- Spanish Project (TIN2015-65464-R)
- Generalitat de Catalunya (2016FI_B 01163)
- COST Action IC1307 iV&L Net (European Network on Integrating Vision and Language) through COST
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- pain assessment, facial expression recognition, long short-term memory networks, deep pain, video sequences, temporal modeling, class imbalance
- MeSH terms
- Emotions, Facial Expression, Humans, Memory, Short-Term, Pain, Neural Networks, Computer
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.
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