{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":44,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":44,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"1cd9bb52caf0","filters":{"venue":"IEEE Transactions on Affective Computing"}},"results":[{"id":"W2036309320","doi":"10.1109/t-affc.2011.28","title":"ECG Pattern Analysis for Emotion Detection","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":469,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Hilbert–Huang transform; Arousal; Valence (chemistry); Speech recognition; Emotion recognition; Affective computing; Computer science; Salience (neuroscience); Artificial intelligence; Modalities; Electroencephalography; Biometrics; Pattern recognition (psychology); Salient; Instantaneous phase; SIGNAL (programming language); Cognitive psychology; Psychology; Social psychology; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.03791127125972519,"gpt":0.2856998968182689,"spread":0.2477886255585437,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002276321,0.0001568424,0.0003125651,0.0005632368,0.0002974467,0.00001535945,0.00004442268,0.00008811917,0.00003764329],"category_scores_gemma":[0.00001226297,0.000150636,0.0005430402,0.000815384,0.00002634887,0.00005544579,6.797586e-7,0.0002117982,0.00001968134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393214,"about_ca_system_score_gemma":0.00001318885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00025092,"about_ca_topic_score_gemma":0.00009859656,"domain_scores_codex":[0.9990291,0.00006812502,0.0001991744,0.0003455611,0.0001436868,0.0002143566],"domain_scores_gemma":[0.9992977,0.0001516175,0.00008875527,0.0002088623,0.000162554,0.00009049307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001610853,0.0004909259,0.01613582,0.00006637872,0.002534386,0.000005484272,0.001053194,0.01022087,0.006640097,6.660404e-7,0.000003996986,0.9626871],"study_design_scores_gemma":[0.001249925,0.0009909956,0.1059331,0.0001126224,0.004676471,0.00001380233,0.0004682853,0.5590885,0.3271602,0.00002595911,0.00001285387,0.0002673247],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3563982,0.000005778496,0.6428358,0.00001518244,0.0002921465,0.0001894487,0.000004304685,0.0001229234,0.0001362601],"genre_scores_gemma":[0.9967463,0.000003907038,0.00286727,0.00005720225,0.0001480122,0.00002412692,0.000003929785,0.00002298838,0.0001262766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9624198,"threshold_uncertainty_score":0.6142759,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3005055041","doi":"10.1109/taffc.2020.3014842","title":"Self-Supervised ECG Representation Learning for Emotion Recognition","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":368,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Transfer of learning; Convolutional neural network; Emotion classification; Multi-task learning; Supervised learning; Machine learning; Transformation (genetics); Feature learning; Task (project management); Deep learning; Artificial neural network; Speech recognition; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06766507257346926,"gpt":0.3295969809081446,"spread":0.2619319083346753,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002609819,0.0002052782,0.000219725,0.0001745401,0.000474666,0.00004970905,0.00007455819,0.0001601871,0.0003369499],"category_scores_gemma":[0.00005109467,0.0002352036,0.0002312066,0.00041497,0.0000264933,0.0001667169,0.000001340598,0.0004296231,0.0004946138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000923054,"about_ca_system_score_gemma":0.00001850628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001626115,"about_ca_topic_score_gemma":0.000004830318,"domain_scores_codex":[0.9982175,0.0004673221,0.0002988462,0.0005733246,0.0001586085,0.0002844177],"domain_scores_gemma":[0.9988503,0.0005263792,0.0001554937,0.0001156466,0.000220879,0.000131265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004159505,0.0004681217,0.0001231777,0.0001030699,0.0002295933,0.000004175592,0.009911316,0.008504879,0.003722382,0.00004052529,0.0003020687,0.9761747],"study_design_scores_gemma":[0.008961821,0.003505127,0.003991163,0.0002331783,0.000443374,0.00005171694,0.008826058,0.9157484,0.0558414,0.0005519614,0.000860152,0.0009857131],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1799847,0.00001020514,0.8137459,0.0004137756,0.001226993,0.0009637867,0.00001478688,0.0006677061,0.002972104],"genre_scores_gemma":[0.9910952,0.000008972779,0.007619774,0.0005987588,0.0003431961,0.00009838488,0.00006867983,0.00005064676,0.0001164137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.975189,"threshold_uncertainty_score":0.9591323,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2345031027","doi":"10.1109/taffc.2016.2515084","title":"Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":327,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; University of Memphis; Bill and Melinda Gates Foundation; National Science Foundation","keywords":"Computer science; Local binary patterns; Artificial intelligence; Classifier (UML); Animation; Facial expression; Computer vision; Pattern recognition (psychology); Histogram; Image (mathematics); Computer graphics (images)","retraction":null,"screen_n_in":null,"score":{"opus":0.043100553927593,"gpt":0.3388566528218517,"spread":0.2957560988942587,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047244,0.0001215815,0.0001862497,0.0002825376,0.0001927426,0.000006384923,0.00003240042,0.00009050602,0.00007515079],"category_scores_gemma":[0.00003750213,0.0001015271,0.00008232654,0.0001924383,0.00008507333,0.0000787098,0.000001376971,0.0001144143,0.00000873422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007526815,"about_ca_system_score_gemma":0.00002363964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005362707,"about_ca_topic_score_gemma":0.000009634577,"domain_scores_codex":[0.9986894,0.0005373257,0.0002736314,0.0002324959,0.0001284665,0.0001386476],"domain_scores_gemma":[0.9989434,0.0005644946,0.0001816838,0.0001205259,0.0001448499,0.00004507057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001330305,0.0002413024,0.00003565052,0.00003545225,0.00005415826,4.908428e-7,0.0005111208,0.02466485,0.7902912,0.00001194268,0.000006266304,0.1840145],"study_design_scores_gemma":[0.001925223,0.000378444,0.01613673,0.0004510909,0.00007000248,0.000006711838,0.0001693945,0.2046337,0.7760431,0.00004024343,0.00001104507,0.000134256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4830498,0.000004006616,0.5161806,0.00001579262,0.0003713789,0.0001953763,0.00001312161,0.00009825961,0.00007165728],"genre_scores_gemma":[0.9978511,0.000001176846,0.002064513,0.00002596436,0.00001876338,0.0000122663,9.623669e-7,0.00001513277,0.00001016125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5148012,"threshold_uncertainty_score":0.4140153,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2779582454","doi":"10.1109/taffc.2017.2784832","title":"Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":266,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Center for Research Resources; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Samsung; NEC Corporation; National Institutes of Health; National Heart, Lung, and Blood Institute","keywords":"Machine learning; Artificial intelligence; Multi-task learning; Computer science; Mood; Leverage (statistics); Feature engineering; Context (archaeology); Deep learning; Task (project management); Psychology; Engineering; Clinical psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.05221817964537573,"gpt":0.3651353464191073,"spread":0.3129171667737316,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005075571,0.0001791106,0.0002421562,0.0001159705,0.002217363,0.000108902,0.0001014989,0.0001011533,0.00007064025],"category_scores_gemma":[0.00004178267,0.0001893821,0.0001252555,0.00004511545,0.0001164459,0.0001213642,0.000002828389,0.0004041867,0.00002007203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007949158,"about_ca_system_score_gemma":0.00002718408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002658837,"about_ca_topic_score_gemma":0.0002246016,"domain_scores_codex":[0.998716,0.0001967134,0.0002035997,0.0004384176,0.0001094017,0.0003358633],"domain_scores_gemma":[0.9989625,0.0003571808,0.0002698701,0.0001983901,0.00009290812,0.0001192054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003069628,0.0006332396,0.01115248,0.0002573788,0.0004025332,0.000005309734,0.0116563,0.003803636,0.0004880663,0.00051232,0.0001919865,0.9705898],"study_design_scores_gemma":[0.04291981,0.008068107,0.5344936,0.004327931,0.0006043417,0.0002761451,0.03258037,0.354636,0.01695882,0.00079698,0.001617829,0.002720031],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2521655,0.00006996047,0.7425392,0.000490748,0.00156563,0.0007519137,0.0000380099,0.0002024707,0.002176578],"genre_scores_gemma":[0.997588,0.00001573889,0.001368272,0.0001326651,0.0001839735,0.00005393403,0.000008774019,0.00003512522,0.0006134813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9678698,"threshold_uncertainty_score":0.9990816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2007041320","doi":"10.1109/t-affc.2013.29","title":"Body Movements for Affective Expression: A Survey of Automatic Recognition and Generation","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":217,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Representation (politics); Affective computing; Expression (computer science); Computer science; Emotion recognition; Notation; Movement (music); State (computer science); Artificial intelligence; Psychology; Human–computer interaction; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07314432565335487,"gpt":0.3265852622291428,"spread":0.2534409365757879,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004502985,0.0001769473,0.0002373447,0.000214408,0.0002294089,0.00003150969,0.00004819243,0.0001288023,0.0003205262],"category_scores_gemma":[0.00004182718,0.0001752137,0.00008522235,0.0001961906,0.00005379386,0.0001578709,0.000001676268,0.0001548267,0.00007990549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005659469,"about_ca_system_score_gemma":0.00001502469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003019132,"about_ca_topic_score_gemma":0.00008309729,"domain_scores_codex":[0.9984765,0.0005328491,0.0002902156,0.000371099,0.0001234449,0.000205874],"domain_scores_gemma":[0.9985437,0.0006952345,0.0001970082,0.0001386195,0.0003547878,0.00007065719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001192344,0.001075542,0.0004721249,0.000133758,0.0002812413,7.312768e-7,0.003199969,0.0003501705,0.08100475,0.00001628806,0.0006347907,0.9127114],"study_design_scores_gemma":[0.008787385,0.003382703,0.3792449,0.0008359777,0.0002024065,0.00003378036,0.001700548,0.1895466,0.4137776,0.001521311,0.00001334812,0.0009534258],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5317373,0.00001249628,0.4662427,0.00001178569,0.0006326693,0.0009856627,0.00005725757,0.00005825598,0.0002618949],"genre_scores_gemma":[0.9978535,0.000005190524,0.001489039,0.00009186262,0.00006762994,0.0002967482,0.00006368264,0.00002715444,0.0001052556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.911758,"threshold_uncertainty_score":0.7145007,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3155665398","doi":"10.1109/taffc.2021.3072579","title":"MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":127,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Overfitting; Maximization; Facial expression; Discriminative model; Computer science; Code (set theory); Artificial intelligence; Deep learning; Pattern recognition (psychology); Machine learning; Mathematics; Mathematical optimization; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.02320631289779015,"gpt":0.291019335747294,"spread":0.2678130228495039,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002380437,0.000216198,0.0002219908,0.0001613389,0.0006680964,0.00006286184,0.00006392713,0.0002092448,0.0004660241],"category_scores_gemma":[0.00002631036,0.0002389625,0.0002205524,0.0004469896,0.00002730192,0.0001224007,0.000002019998,0.000280107,0.00007728134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000132031,"about_ca_system_score_gemma":0.00002973378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000307388,"about_ca_topic_score_gemma":0.0006190527,"domain_scores_codex":[0.9982764,0.0003880969,0.0003172649,0.0005400285,0.0001452802,0.000332923],"domain_scores_gemma":[0.9987355,0.000430425,0.000191459,0.0002209366,0.0003347978,0.00008680846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004903358,0.0008214092,0.001374426,0.00006747056,0.0002973897,0.000006647679,0.001402527,0.1126145,0.00244553,0.0002084695,0.0003360519,0.8799352],"study_design_scores_gemma":[0.01077491,0.001196008,0.07487576,0.0005998622,0.0006487374,0.0002182102,0.001733543,0.7193373,0.1839009,0.00352746,0.001657478,0.001529898],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321315,0.00006160231,0.8617721,0.00005367936,0.004199887,0.0006215258,0.00001642902,0.0002447593,0.0008985584],"genre_scores_gemma":[0.9942557,0.000006879792,0.004495558,0.0001667702,0.000433295,0.000134173,0.0001332523,0.00004774333,0.0003266107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8784053,"threshold_uncertainty_score":0.9744607,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2404236138","doi":"10.1109/taffc.2015.2457893","title":"Design and Evaluation of a Touch-Centered Calming Interaction with a Social Robot","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Human–computer interaction; Robot; Human–robot interaction; Computer science; Robotics; Social robot; Breathing; Rehabilitation robotics; Component (thermodynamics); Psychology; Artificial intelligence; Robot control; Mobile robot","retraction":null,"screen_n_in":null,"score":{"opus":0.1828447379574947,"gpt":0.416848753491572,"spread":0.2340040155340773,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005843917,0.0001563856,0.0002217643,0.0001991242,0.0002097194,0.00003086063,0.00005485753,0.00009351883,0.0001115381],"category_scores_gemma":[0.0000202304,0.0001522968,0.00006425509,0.0002418985,0.00007375691,0.0001372467,0.00000150615,0.0002872159,0.00002333271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002835343,"about_ca_system_score_gemma":0.0000699848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002044744,"about_ca_topic_score_gemma":0.0000549668,"domain_scores_codex":[0.998231,0.0007449066,0.0002186559,0.0002938753,0.0003274374,0.0001841603],"domain_scores_gemma":[0.998814,0.0004019638,0.0001914828,0.0001003973,0.000417518,0.0000746097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003723532,0.001334677,0.0003155241,0.00003048569,0.0008624715,0.000009331875,0.08010472,0.1945979,0.004858589,0.0002534603,0.0005021318,0.7134072],"study_design_scores_gemma":[0.03073147,0.006870279,0.02447437,0.0009359859,0.001933266,0.0004770485,0.08901671,0.7833818,0.05944992,0.0006857592,0.0002902319,0.001753139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3304605,0.00001220763,0.6664692,0.00006667503,0.000928033,0.0004211358,0.00000157209,0.00005699093,0.001583615],"genre_scores_gemma":[0.9983003,6.373854e-7,0.00142115,0.00005928769,0.00009871797,0.00004302514,0.00000130597,0.00002324756,0.00005233851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.711654,"threshold_uncertainty_score":0.6210485,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3083323904","doi":"10.1109/taffc.2020.3021755","title":"A Deep Multiscale Spatiotemporal Network for Assessing Depression From Facial Dynamics","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":106,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Academy of Finland","keywords":"Dynamics (music); Depression (economics); Facial expression; Artificial intelligence; Computer science; Psychology; Cognitive psychology; Statistical physics; Physics; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.03741382043883733,"gpt":0.3237295399251854,"spread":0.286315719486348,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001331911,0.0002024341,0.0002433674,0.00006137294,0.0004865285,0.00006408886,0.0000945542,0.0001875099,0.0001566061],"category_scores_gemma":[0.00001769522,0.0002154771,0.0001935349,0.0002078347,0.00004005726,0.0001217492,0.000002277427,0.0003727358,0.00007627314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009706182,"about_ca_system_score_gemma":0.00001814172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001182609,"about_ca_topic_score_gemma":0.0002400122,"domain_scores_codex":[0.9985609,0.0002454861,0.0002570379,0.0005024435,0.0001277409,0.0003063883],"domain_scores_gemma":[0.9989281,0.0005819675,0.0001478804,0.0001244693,0.00008446658,0.0001330927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002961404,0.0002717305,0.002083269,0.00002114594,0.0001492457,0.000006164265,0.002566243,0.06582972,0.0004822368,0.00004052962,0.0002536247,0.928],"study_design_scores_gemma":[0.002572776,0.0002496677,0.0208428,0.0001267167,0.0001038882,0.000004474689,0.000946072,0.9724954,0.001920964,0.0002537304,0.0001418274,0.0003416715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1743874,0.00002584745,0.8214793,0.000187203,0.002166192,0.0005609781,0.00006521015,0.0002370166,0.0008908064],"genre_scores_gemma":[0.9847158,0.000001224217,0.01392293,0.0005006883,0.0006652286,0.00004085112,0.00007911579,0.00004299848,0.00003112941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9276583,"threshold_uncertainty_score":0.8786899,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4214576234","doi":"10.1109/taffc.2022.3154332","title":"Prediction of Depression Severity Based on the Prosodic and Semantic Features With Bidirectional LSTM and Time Distributed CNN","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Mental Health via Writing","field":"Psychology","cited_by":90,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Computer science; Depression (economics); Semantics (computer science); Natural language processing; Artificial intelligence; Speech recognition; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.01726815988762468,"gpt":0.2741622699566435,"spread":0.2568941100690188,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005056393,0.0001484736,0.0001690346,0.0001244766,0.001072813,0.00001555244,0.00006097805,0.00004942349,0.0001440495],"category_scores_gemma":[0.000009615225,0.0001164334,0.0000362278,0.0003099334,0.0001130454,0.0000376299,0.000005093811,0.0005252965,0.00000248323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001247758,"about_ca_system_score_gemma":0.00002957913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000110496,"about_ca_topic_score_gemma":0.000008980153,"domain_scores_codex":[0.9983905,0.000602667,0.0001724484,0.0003688067,0.0002686582,0.0001969275],"domain_scores_gemma":[0.9986184,0.0009924613,0.00013207,0.0001596575,0.00004013187,0.00005723838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01778149,0.01017196,0.2092829,0.001798866,0.001474836,0.0001328293,0.01839754,0.2838979,0.04037937,0.0009889464,0.003502367,0.4121909],"study_design_scores_gemma":[0.003061394,0.002814467,0.7147777,0.0005139679,0.0001328694,0.0003143969,0.00158094,0.2658387,0.01050111,0.00009073548,0.00005523854,0.0003184626],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9097919,0.00005145786,0.08790026,0.0002011576,0.0003150839,0.0008336696,0.000228436,0.00009885171,0.0005791434],"genre_scores_gemma":[0.9995123,0.000001047401,0.0001371553,0.0001347938,0.00003021731,0.0001023526,0.00001350941,0.000018243,0.00005034278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5054948,"threshold_uncertainty_score":0.8251322,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297939181","doi":"10.1109/taffc.2022.3210441","title":"PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Pairwise comparison; Artificial intelligence; Computer science; Parsing; Pattern recognition (psychology); Labeled data; Valence (chemistry); Representation (politics); Natural language processing; Electroencephalography; Speech recognition; Machine learning; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.04876540291933584,"gpt":0.3241300922183247,"spread":0.2753646892989889,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007107399,0.0001569578,0.0002259179,0.0004652768,0.0004690722,0.00001237817,0.00008137004,0.00007396288,0.0007688397],"category_scores_gemma":[0.00002857749,0.0001950043,0.0001949898,0.0005231783,0.00003814076,0.00008274574,0.000003526775,0.0004453752,0.00002629724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002377779,"about_ca_system_score_gemma":0.00002737244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009697891,"about_ca_topic_score_gemma":0.00003077473,"domain_scores_codex":[0.9978048,0.0008792219,0.0004092769,0.0004350714,0.0002115969,0.0002599905],"domain_scores_gemma":[0.9987926,0.0006805434,0.0002030984,0.0001573364,0.0001151062,0.00005134316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006591788,0.002588361,0.001464728,0.0000978141,0.0002169422,0.000008831173,0.01487477,0.3202834,0.009958624,0.0001343523,0.000296507,0.6494165],"study_design_scores_gemma":[0.03548163,0.01059928,0.09447861,0.001070575,0.0007770584,0.0002346377,0.1240299,0.6272045,0.09718982,0.005118092,0.001262332,0.002553513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5073175,0.00001481605,0.4893037,0.00008777032,0.0009999439,0.0008470959,0.00004794952,0.00009125796,0.001289973],"genre_scores_gemma":[0.9983336,0.000006769044,0.0006836409,0.00008399189,0.00004254813,0.0004316936,0.00009067245,0.00003316024,0.0002939356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.646863,"threshold_uncertainty_score":0.8418255,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2883496341","doi":"10.1109/taffc.2018.2858255","title":"Feature Pooling of Modulation Spectrum Features for Improved Speech Emotion Recognition in the Wild","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Reverberation; Pooling; Computer science; Speech recognition; Benchmark (surveying); Affective computing; Noise (video); Feature (linguistics); Valence (chemistry); Background noise; Artificial intelligence; Feature extraction; Engineering; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.02867653713525803,"gpt":0.3116998049138324,"spread":0.2830232677785743,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063681,0.0001810844,0.0001988853,0.0003302602,0.0002807596,0.00002896177,0.0001067155,0.000203844,0.00005222023],"category_scores_gemma":[0.00002798641,0.0001562216,0.0001652933,0.0004624648,0.00007896773,0.0001088659,0.000001046186,0.0003934518,0.0000251319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008520892,"about_ca_system_score_gemma":0.00001588055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008733662,"about_ca_topic_score_gemma":0.0003310394,"domain_scores_codex":[0.9986768,0.0003322581,0.0002274846,0.000369179,0.0001273014,0.0002669248],"domain_scores_gemma":[0.9989511,0.0004493799,0.0001904242,0.0001964734,0.0001822135,0.00003039504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006710119,0.0007039527,0.00006787459,0.00005523861,0.0001173682,0.000001673355,0.006367992,0.0009595028,0.01173142,0.0001875888,0.0005398195,0.9785966],"study_design_scores_gemma":[0.01938403,0.01059461,0.285642,0.002017106,0.0007064206,0.0005410935,0.01533927,0.2383239,0.4037006,0.02097705,0.0006104668,0.002163404],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3928598,0.00001139622,0.6026707,0.0006204379,0.001468917,0.0009596157,0.00003473129,0.0000649633,0.001309529],"genre_scores_gemma":[0.9969174,0.000003799672,0.002121571,0.0002815563,0.0004507274,0.00004015979,0.0000350906,0.00002684781,0.0001228303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9764332,"threshold_uncertainty_score":0.6370532,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078277829","doi":"10.1109/t-affc.2012.30","title":"Projection into Expression Subspaces for Face Recognition from Single Sample per Person","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Linear subspace; Subspace topology; Expression (computer science); Artificial intelligence; Projection (relational algebra); Pattern recognition (psychology); Linear discriminant analysis; Facial recognition system; Face (sociological concept); Facial expression; Computer science; Sample (material); Set (abstract data type); Biometrics; Discriminant; Image (mathematics); Computer vision; Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.05310404105780693,"gpt":0.2796202461226605,"spread":0.2265162050648536,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003307531,0.0002671025,0.0002264896,0.0002430787,0.0007826855,0.0001639238,0.0002217912,0.000165649,0.00003940715],"category_scores_gemma":[0.00004687009,0.000255234,0.0001875693,0.0003032132,0.00003667413,0.001248349,0.000006135581,0.0002727478,0.0001058613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002090488,"about_ca_system_score_gemma":0.00002560405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002598728,"about_ca_topic_score_gemma":0.00003861947,"domain_scores_codex":[0.9982696,0.0002348648,0.0002104287,0.0005630956,0.000272692,0.0004493757],"domain_scores_gemma":[0.9981056,0.001149785,0.0001605452,0.0002722394,0.0001669525,0.0001449083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001124291,0.0007060321,0.0001194423,0.00004644841,0.00004758394,3.118164e-7,0.0123143,0.001550926,0.3052736,0.00001045102,0.0003424815,0.679476],"study_design_scores_gemma":[0.0009506287,0.0004620853,0.0005903788,0.0003332008,0.00004175873,0.000006192919,0.002324059,0.08294087,0.9107665,0.0006396172,0.000451913,0.0004928204],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2696253,0.00004918615,0.7275946,0.0001243223,0.001552033,0.0005821505,0.00003394042,0.0003296209,0.000108881],"genre_scores_gemma":[0.8515437,0.000005951914,0.1478969,0.0001147868,0.0002478346,0.00011259,0.00002791196,0.00002538493,0.0000250073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6789832,"threshold_uncertainty_score":0.99999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4396242939","doi":"10.1109/taffc.2024.3394436","title":"Spectral-Spatial Attention Alignment for Multi-Source Domain Adaptation in EEG-Based Emotion Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":43,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Sciences and Engineering Research Council of Canada","keywords":"Electroencephalography; Emotion recognition; Adaptation (eye); Computer science; Domain adaptation; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Domain (mathematical analysis); Psychology; Cognitive psychology; Neuroscience; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.05410333939053946,"gpt":0.3020428935639402,"spread":0.2479395541734007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000420026,0.0002279615,0.0001814765,0.0004345491,0.0002653378,0.0001595671,0.0001037887,0.00009512267,0.00001400141],"category_scores_gemma":[0.00002806404,0.0002364816,0.0001903627,0.0004112423,0.0000499967,0.0002223035,0.000001754899,0.0002989984,0.00004615649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003644303,"about_ca_system_score_gemma":0.00003772129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007849293,"about_ca_topic_score_gemma":0.0001576482,"domain_scores_codex":[0.9980967,0.0003499871,0.0003177435,0.0006879458,0.0002341654,0.0003134382],"domain_scores_gemma":[0.998792,0.000911233,0.00008296213,0.0001146648,0.00004537212,0.0000537534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000178552,0.0005532631,0.00002556285,0.0001601595,0.00002154091,0.00001230841,0.002204669,0.3038778,0.2988164,0.00005489474,0.00001821123,0.3940767],"study_design_scores_gemma":[0.0009377042,0.0003575133,0.0007394222,0.0004299438,0.00001684663,0.00000830484,0.0001989833,0.7033787,0.2935223,0.0001877915,0.00003783187,0.0001845877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.401493,0.000007735303,0.5963143,0.000133182,0.001119273,0.0006601235,0.00002590177,0.0002190456,0.0000274744],"genre_scores_gemma":[0.9905436,0.000002799183,0.009000101,0.0001650119,0.0001123085,0.00007667715,0.00001125855,0.00003832513,0.00004990242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5890507,"threshold_uncertainty_score":0.9643441,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3114806760","doi":"10.1109/taffc.2020.3047582","title":"A Multi-Modal Stacked Ensemble Model for Bipolar Disorder Classification","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Stuttering Research and Treatment","field":"Psychology","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Hyperparameter; Computer science; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Convolutional neural network; Speech recognition; Perceptron; Discriminant; Feature selection; Artificial neural network; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.1026109598210847,"gpt":0.3707405420403738,"spread":0.2681295822192892,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001244842,0.0001905727,0.0001947891,0.000102943,0.0003526489,0.00003426422,0.0001208181,0.00008416716,0.00004097667],"category_scores_gemma":[0.00001783188,0.0001867483,0.0001646828,0.0002150474,0.00004923794,0.0000567904,0.000002097517,0.0002524396,0.0001434951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174801,"about_ca_system_score_gemma":0.00003705767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005834126,"about_ca_topic_score_gemma":0.00003745659,"domain_scores_codex":[0.9986687,0.0001176992,0.0001744939,0.0005044977,0.0001445464,0.0003901106],"domain_scores_gemma":[0.9991085,0.0003852992,0.00005880708,0.000201679,0.00008935408,0.0001563479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001300528,0.002305108,0.001009706,0.0000959872,0.0007785347,0.00001269847,0.01440445,0.1316189,0.02628184,0.0004293263,0.0003709908,0.821392],"study_design_scores_gemma":[0.002692182,0.0006058432,0.005202461,0.00001782381,0.00004561332,0.00000264969,0.0004355278,0.9881738,0.002336881,0.00003423187,0.0002645266,0.0001885048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1821637,0.00009476877,0.8158989,0.0004313085,0.0002383997,0.0008604446,0.00006362063,0.0001694279,0.00007950218],"genre_scores_gemma":[0.9909749,0.000006754339,0.008280873,0.0002000285,0.00006373357,0.0002297393,0.000007819242,0.00004210959,0.0001940278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8565549,"threshold_uncertainty_score":0.7615374,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3104660304","doi":"10.1109/taffc.2020.3023966","title":"An Emotion Recognition Method for Game Evaluation Based on Electroencephalogram","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":34,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"Science and Technology Planning Project of Guangdong Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Emotion recognition; Electroencephalography; Computer science; Artificial intelligence; Speech recognition; Emotion classification; Psychology; Pattern recognition (psychology); Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.08136300908645765,"gpt":0.3918630395853451,"spread":0.3105000304988874,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00102884,0.0002543822,0.000240453,0.000277429,0.0003020185,0.00005319122,0.0001030896,0.0001889846,0.0004927897],"category_scores_gemma":[0.00006329417,0.0002762052,0.0002294841,0.0004617441,0.00003193337,0.0001432444,4.54883e-7,0.0004057662,0.0001510671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001940843,"about_ca_system_score_gemma":0.00004861713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001928376,"about_ca_topic_score_gemma":0.00001404434,"domain_scores_codex":[0.9970069,0.001306291,0.0002948176,0.0007321473,0.0003084602,0.0003513764],"domain_scores_gemma":[0.9983846,0.0006788727,0.0001723741,0.000202733,0.0003887738,0.0001726667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006571765,0.0008140239,0.000008327183,0.00002662739,0.0000730418,0.000001227308,0.001387111,0.07434865,0.006996645,0.00004181865,0.0001182395,0.9155271],"study_design_scores_gemma":[0.00326893,0.005036275,0.001446926,0.000078344,0.0002052391,0.000007419626,0.0004767431,0.9682286,0.02041393,0.0004628527,0.0000667386,0.0003079946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06488546,0.000006666332,0.929813,0.0005372959,0.001182453,0.001834711,0.00005247673,0.0004038377,0.001284132],"genre_scores_gemma":[0.97838,0.000001288035,0.01853316,0.002278161,0.0003347915,0.000264189,0.0001456962,0.00005094219,0.00001183102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9152191,"threshold_uncertainty_score":0.999969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2528939255","doi":"10.1109/taffc.2017.2763132","title":"Feature Learning from Spectrograms for Assessment of Personality Traits","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Spectrogram; Computer science; Feature extraction; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Speech recognition; Set (abstract data type); Linear discriminant analysis; Parameterized complexity; Process (computing); Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.03554585514234741,"gpt":0.3259179887493793,"spread":0.2903721336070319,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003635002,0.0001826159,0.0002795017,0.00007579799,0.001277936,0.0003273762,0.0006310611,0.00008253237,0.000005054606],"category_scores_gemma":[0.00003204292,0.0001785326,0.000226127,0.0001138779,0.00007952782,0.000367172,0.000007660988,0.0004305427,0.000001771558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009625001,"about_ca_system_score_gemma":0.00008108422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006012125,"about_ca_topic_score_gemma":0.00004072725,"domain_scores_codex":[0.9987387,0.00009047936,0.000141797,0.0004865289,0.0002457375,0.0002967627],"domain_scores_gemma":[0.9986885,0.0004502327,0.0003065219,0.0003349935,0.0001433646,0.00007644446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002860189,0.0002607472,0.001677388,0.00006188684,0.0001669989,0.000004962428,0.001073456,0.01211271,0.0347638,0.0002502121,0.00003431517,0.9495649],"study_design_scores_gemma":[0.001683463,0.0006256594,0.1136378,0.0003412389,0.00006044363,0.000009168797,0.0001719986,0.3811418,0.5006208,0.00109609,0.0001733003,0.0004382635],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.197146,0.00002435093,0.8011419,0.0004148831,0.0003985619,0.0002304162,0.000007688831,0.0001211702,0.0005149586],"genre_scores_gemma":[0.851099,0.000002133087,0.1486866,0.00003747406,0.0001023955,0.00001024957,0.000001124059,0.00001201947,0.00004901057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9491267,"threshold_uncertainty_score":0.982898,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2949488638","doi":"10.1109/taffc.2019.2922911","title":"Participatory Design of Affective Technology: Interfacing Biomusic and Autism","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; University of California, Davis; University of Toronto","keywords":"Interfacing; Participatory design; Autism; Citizen journalism; Psychology; Affective computing; Cognitive psychology; Human–computer interaction; Cognitive science; Computer science; Engineering; Developmental psychology; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.03069393605463817,"gpt":0.2838684851335543,"spread":0.2531745490789162,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005165322,0.0002740645,0.00037281,0.001174217,0.000241702,0.00004414969,0.0003948183,0.0002220664,0.00001602443],"category_scores_gemma":[0.00002810169,0.0002816317,0.00005946759,0.001395956,0.0002626468,0.000347276,0.00003413747,0.0007460867,0.00007206252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002186144,"about_ca_system_score_gemma":0.00006156485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001643775,"about_ca_topic_score_gemma":0.000005133625,"domain_scores_codex":[0.9982079,0.000251698,0.0003285391,0.000656515,0.0001823252,0.0003730379],"domain_scores_gemma":[0.9983642,0.0006510497,0.0002557496,0.0004894613,0.0001989798,0.00004061354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009744981,0.0004815589,0.0007573946,0.0001123508,0.0004370851,0.00001871223,0.003742538,0.02568164,0.5052885,0.02940717,0.00003254124,0.433943],"study_design_scores_gemma":[0.0008763284,0.00128825,0.001718299,0.0003351737,0.00003004385,0.0000740839,0.0003648236,0.276006,0.7144131,0.004511115,0.00001286395,0.0003698597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4280095,0.00002353111,0.5703316,0.0001035647,0.000645603,0.0004230797,0.000001559231,0.000315467,0.0001461775],"genre_scores_gemma":[0.987406,0.000002325132,0.01243184,0.00006890208,0.00001017553,0.00003212173,2.18623e-7,0.00002170654,0.00002674248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5593965,"threshold_uncertainty_score":0.9999636,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2898544765","doi":"10.1109/taffc.2018.2878029","title":"Using Temporal Features of Observers’ Physiological Measures to Distinguish Between Genuine and Fake Smiles","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Alberta; Indian Institute of Science","keywords":"Skin conductance; Computer science; Observer (physics); Artificial intelligence; Pattern recognition (psychology); Benchmark (surveying); Feature extraction; Speech recognition; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1363811192801346,"gpt":0.3700087618898636,"spread":0.233627642609729,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000238393,0.0001718576,0.000273431,0.0001510207,0.0002786179,0.00001783403,0.00008144829,0.0001199801,0.00005907709],"category_scores_gemma":[0.00003691235,0.0001573028,0.0000976636,0.0002465303,0.0001695011,0.0000360511,0.000004010994,0.000224619,0.00001716092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003967875,"about_ca_system_score_gemma":0.00001174368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002168708,"about_ca_topic_score_gemma":0.00009455146,"domain_scores_codex":[0.9987933,0.0002669781,0.0002100622,0.0003695318,0.0001326828,0.0002274633],"domain_scores_gemma":[0.9992369,0.0002322555,0.0001059411,0.0001401983,0.0001847366,0.00009997232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005900036,0.001033208,0.01855083,0.0001226792,0.0008463401,0.00001581226,0.01328998,0.001613888,0.09047279,0.0002975558,0.000948809,0.8722181],"study_design_scores_gemma":[0.001498032,0.002003415,0.9448122,0.0003488182,0.0001790903,0.00003078428,0.001313336,0.0007294099,0.0481659,0.0002906994,0.0001519583,0.0004763729],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6748084,0.00001892031,0.3235421,0.00002789833,0.0006077531,0.0002222271,0.00003079836,0.00006971532,0.0006721505],"genre_scores_gemma":[0.9967301,0.000001111504,0.00268793,0.0001040736,0.0004004527,0.000004814673,0.000004193683,0.00001862582,0.00004873282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9262614,"threshold_uncertainty_score":0.6414622,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3020653641","doi":"10.1109/taffc.2020.2988455","title":"A Multimodal Non-Intrusive Stress Monitoring From the Pleasure-Arousal Emotional Dimensions","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Modality (human–computer interaction); Computer science; Artificial intelligence; Affective computing; Arousal; Machine learning; Valence (chemistry); Operator (biology); Support vector machine; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.03606804612578591,"gpt":0.3006937815587699,"spread":0.264625735432984,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001396155,0.0002599472,0.0002367346,0.00007047478,0.0007163191,0.00005361257,0.0001861339,0.0001466855,0.0003994275],"category_scores_gemma":[0.00003199486,0.0002209618,0.0002197592,0.0003063114,0.0000922251,0.00008833475,0.000005233824,0.0007995676,0.0005354076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008742521,"about_ca_system_score_gemma":0.00003541711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003106798,"about_ca_topic_score_gemma":0.0000458066,"domain_scores_codex":[0.9981645,0.0004022023,0.0002640391,0.0005543074,0.0002827396,0.0003321737],"domain_scores_gemma":[0.9982047,0.001129514,0.0001215404,0.0002179649,0.0001524681,0.0001738019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001708866,0.003996935,0.02130911,0.00007895398,0.003395103,0.0001944081,0.1051042,0.1959821,0.04735018,0.0004302108,0.002496656,0.6179532],"study_design_scores_gemma":[0.01181936,0.001916357,0.6981912,0.00128503,0.0007197396,0.0000789511,0.02641573,0.1694018,0.08751379,0.000379166,0.0003352428,0.001943646],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4918658,0.00002455047,0.5036121,0.0006292614,0.0025155,0.0003834469,0.0001276692,0.0001709459,0.0006707144],"genre_scores_gemma":[0.9973643,0.000004752363,0.0008604667,0.0006462228,0.0009942747,0.00003348276,0.00001621923,0.00004186866,0.00003841725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.676882,"threshold_uncertainty_score":0.9010561,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285235436","doi":"10.1109/taffc.2022.3188223","title":"Quality-Aware Bag of Modulation Spectrum Features for Robust Speech Emotion Recognition","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Speech recognition; Emotion recognition; Quality (philosophy); Modulation (music); Computer science; Spectrum (functional analysis); Artificial intelligence; Pattern recognition (psychology); Physics; Acoustics","retraction":null,"screen_n_in":null,"score":{"opus":0.03871749685793811,"gpt":0.2867100582871423,"spread":0.2479925614292042,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007411915,0.0001769407,0.0002400232,0.0003221723,0.0008946727,0.00007824432,0.0002994206,0.00005622141,0.0000216499],"category_scores_gemma":[0.00002116872,0.0002020706,0.0001905877,0.0007176677,0.0000292741,0.000344894,0.000009911113,0.0003378239,0.000003642626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002437249,"about_ca_system_score_gemma":0.00006336443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004041739,"about_ca_topic_score_gemma":0.00002213545,"domain_scores_codex":[0.9982519,0.0002751158,0.0003131451,0.0005045994,0.0003775006,0.000277762],"domain_scores_gemma":[0.9988004,0.0004502804,0.0002823973,0.0002587823,0.0001561073,0.00005203926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005702123,0.000218141,0.00002943136,0.00006530027,0.00003395618,0.00000157167,0.0005872619,0.3343589,0.007953464,0.00005926997,0.00003839092,0.6565973],"study_design_scores_gemma":[0.001132968,0.0006538213,0.003635258,0.00009320222,0.0000233572,0.00005058164,0.0003723384,0.4546197,0.5347996,0.004240287,0.00001833691,0.0003605535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1519208,0.00001618188,0.8460926,0.0002511039,0.0008920493,0.0004647046,0.00003531644,0.000204059,0.000123256],"genre_scores_gemma":[0.9209468,0.000001714415,0.07876191,0.00009574733,0.00008478752,0.00003432576,0.00001315293,0.00001844259,0.00004309733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.769026,"threshold_uncertainty_score":0.8240198,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2905178934","doi":"10.1109/taffc.2018.2885744","title":"Using Circular Models to Improve Music Emotion Recognition","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Music and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Categorical variable; Computer science; Valence (chemistry); Affect (linguistics); Artificial intelligence; Affective computing; Arousal; Regression; Natural language processing; Pattern recognition (psychology); Machine learning; Mathematics; Psychology; Statistics; Communication","retraction":null,"screen_n_in":null,"score":{"opus":0.07868070166492959,"gpt":0.2848457243521875,"spread":0.2061650226872579,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003817892,0.0002256026,0.0002046025,0.0003150359,0.0008022339,0.0002137997,0.0003109225,0.00009196393,0.00001765892],"category_scores_gemma":[0.000008930426,0.0002424799,0.0001174233,0.0008696226,0.00006062899,0.0007459647,0.000009981844,0.0002517498,0.0001132866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002244578,"about_ca_system_score_gemma":0.00007393677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004768696,"about_ca_topic_score_gemma":0.000009043709,"domain_scores_codex":[0.9982499,0.0001371894,0.0002398102,0.0006979542,0.0002780526,0.0003970577],"domain_scores_gemma":[0.9989738,0.0001048024,0.0001184827,0.0003555549,0.0003089355,0.000138433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000158797,0.0001379582,0.000001991945,0.00003552102,0.0000402886,0.00000526606,0.003754575,0.06308798,0.07757997,0.0001091774,0.0000401766,0.8551912],"study_design_scores_gemma":[0.0003214215,0.0002470014,0.00004642199,0.000207501,0.00002092076,0.00002482752,0.00005852939,0.8612899,0.1350035,0.002460915,0.0000181235,0.0003009828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2175618,0.00000586943,0.7795669,0.00009180453,0.001493191,0.0002997637,0.000003022133,0.0002803943,0.000697209],"genre_scores_gemma":[0.916408,5.981915e-7,0.08207592,0.001162565,0.0003075938,0.000007987313,5.390337e-7,0.00002237275,0.00001442274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8548902,"threshold_uncertainty_score":0.9888045,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409760880","doi":"10.1109/taffc.2025.3564272","title":"Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-Based Emotion Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Sciences and Engineering Research Council of Canada; Universidade de Macau; National Natural Science Foundation of China","keywords":"Electroencephalography; Emotion recognition; Computer science; Domain adaptation; Adaptation (eye); Artificial intelligence; Cognitive psychology; Domain (mathematical analysis); Psychology; Speech recognition; Pattern recognition (psychology); Natural language processing; Mathematics; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.02959378115604441,"gpt":0.3006888252497074,"spread":0.271095044093663,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005009216,0.0002219361,0.0002179144,0.0005261318,0.0004751818,0.00005339533,0.0001018541,0.0001799119,0.00007667279],"category_scores_gemma":[0.00004475585,0.0002021383,0.0001620466,0.0007295155,0.0000551404,0.0001085454,0.000001404986,0.0004738785,0.00002634541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002122651,"about_ca_system_score_gemma":0.00005382999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006539818,"about_ca_topic_score_gemma":0.0003850471,"domain_scores_codex":[0.9981744,0.0005520225,0.0003686718,0.0004572176,0.0001335012,0.0003141662],"domain_scores_gemma":[0.9982554,0.001183248,0.0001635218,0.0001858397,0.0001764713,0.00003552447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004134795,0.0004798188,0.0002436011,0.0001172804,0.0001310654,0.00000334653,0.004766644,0.02506945,0.002422704,0.001357008,0.00005334282,0.9649423],"study_design_scores_gemma":[0.02747042,0.002160724,0.143437,0.004101722,0.0007100942,0.00005929264,0.05053959,0.6532099,0.05056159,0.06565393,0.0002042801,0.001891393],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3348269,0.00001584726,0.6612412,0.0004109486,0.001605141,0.00103337,0.00002872266,0.0001138061,0.0007241218],"genre_scores_gemma":[0.9975577,0.000001975965,0.001459149,0.0006193949,0.0000956756,0.0001447327,0.00004759705,0.00002819138,0.00004559689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9630508,"threshold_uncertainty_score":0.8242959,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4321021222","doi":"10.1109/taffc.2023.3244520","title":"When is a Haptic Message Like an Inside Joke? Digitally Mediated Emotive Communication Builds on Shared History","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Haptic technology; Emotive; Context (archaeology); Set (abstract data type); Wearable computer; Human–computer interaction; Interpersonal communication; Computer science; Joke; Matching (statistics); Interpretation (philosophy); Psychology; Social psychology; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.05605038605737767,"gpt":0.2889904778145967,"spread":0.2329400917572191,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001491567,0.0003410309,0.0003133911,0.0006405874,0.0007466406,0.0001295337,0.0004246952,0.0001470994,0.0002413786],"category_scores_gemma":[0.0002213585,0.0003652713,0.0002094771,0.0006167269,0.000170103,0.0007540944,0.00001013203,0.0008947499,0.0009002173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007673008,"about_ca_system_score_gemma":0.0000922727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001468185,"about_ca_topic_score_gemma":0.0001446822,"domain_scores_codex":[0.9973775,0.0006292011,0.0003572821,0.0007686554,0.0004301273,0.0004372402],"domain_scores_gemma":[0.9956799,0.003009002,0.0002555251,0.0007362475,0.0001334632,0.000185824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001089506,0.004175066,0.0001202541,0.0001190665,0.0004201452,0.0002942016,0.1018802,0.07407296,0.6932076,0.0004418161,0.01908175,0.1050975],"study_design_scores_gemma":[0.003837392,0.002998026,0.006675437,0.000977309,0.0002270978,0.0001521701,0.004285395,0.5440043,0.4162621,0.00131012,0.01707538,0.002195322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8868163,0.00001057896,0.09758899,0.0008409384,0.002074616,0.0009665855,0.000198728,0.001722965,0.009780293],"genre_scores_gemma":[0.9964487,0.00002533354,0.0001301906,0.002169885,0.00005027671,0.00004639107,0.00001782599,0.00007159422,0.001039833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4699313,"threshold_uncertainty_score":0.9998799,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312806248","doi":"10.1109/taffc.2022.3221922","title":"Using Affect as a Communication Modality to Improve Human-Robot Communication in Robot-Assisted Search and Rescue Scenarios","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Modality (human–computer interaction); Situation awareness; Robot; Context (archaeology); Computer science; Search and rescue; Human–computer interaction; Affect (linguistics); Human–robot interaction; Affective computing; Artificial intelligence; Psychology; Communication; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.106127067829452,"gpt":0.4354304591443012,"spread":0.3293033913148493,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001328687,0.0002351238,0.0003387092,0.0005022253,0.001921753,0.00009250376,0.000361742,0.0001195,0.0002642114],"category_scores_gemma":[0.00002920536,0.0002950225,0.000129458,0.0008032208,0.00009714691,0.0001378357,0.00004432822,0.001396797,0.00003661171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009798213,"about_ca_system_score_gemma":0.00006076549,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0136229,"about_ca_topic_score_gemma":0.002769663,"domain_scores_codex":[0.9955213,0.002872794,0.0004006169,0.0005399883,0.000285661,0.0003796206],"domain_scores_gemma":[0.9981673,0.0007486537,0.0001029303,0.0007385212,0.0001214826,0.000121163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008959737,0.002086326,0.001360764,0.00004580124,0.0002775907,0.00001472148,0.03620468,0.7903246,0.06016326,0.001665584,0.0000583347,0.1069023],"study_design_scores_gemma":[0.01248342,0.003892572,0.4814386,0.0008880846,0.0003345188,0.0003710961,0.08450232,0.3984225,0.0137961,0.001112606,0.000207286,0.002550874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8310382,0.00006659592,0.1653188,0.0006230709,0.0005186598,0.001174465,0.00001521608,0.0001434669,0.001101561],"genre_scores_gemma":[0.9974503,0.000004644549,0.00172162,0.0003626443,0.00002998686,0.0001814713,0.00001050429,0.00004614006,0.0001926842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4800779,"threshold_uncertainty_score":0.9999502,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2965082101","doi":"10.1109/taffc.2019.2931689","title":"Psychophysiological Reactions to Persuasive Messages Deploying Persuasion Principles","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Waterloo; European Commission","keywords":"Persuasion; Psychology; Skin conductance; Psychological intervention; Reactivity (psychology); Psychophysiology; Persuasive technology; Social psychology; Medicine; Neuroscience; Psychiatry","retraction":null,"screen_n_in":null,"score":{"opus":0.06875254208585342,"gpt":0.3876376047391193,"spread":0.3188850626532659,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002913908,0.0002833417,0.000324761,0.0003382706,0.0005643399,0.00003984674,0.0002153414,0.0001936185,0.00170624],"category_scores_gemma":[0.00001474836,0.0002680807,0.0003494411,0.0005011575,0.00005562328,0.0001208914,0.00000551586,0.0007253515,0.002866233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002385169,"about_ca_system_score_gemma":0.0000289328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002048219,"about_ca_topic_score_gemma":0.00003272789,"domain_scores_codex":[0.9977255,0.0003541644,0.0003970536,0.0007514798,0.0002117976,0.0005599719],"domain_scores_gemma":[0.9986128,0.0004241782,0.0001313029,0.000439197,0.0001357412,0.000256784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002987078,0.007998645,0.007032258,0.0002188223,0.0006928641,0.00006440602,0.02102923,0.05062512,0.0889211,0.005456452,0.002873917,0.8121001],"study_design_scores_gemma":[0.01046693,0.02013094,0.8699642,0.003083667,0.0004892111,0.000294305,0.0326728,0.01419066,0.02323325,0.0003211293,0.02129457,0.003858378],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.827045,0.00003043678,0.1622865,0.0002486325,0.003501289,0.0009659714,0.00002083081,0.000334216,0.005567089],"genre_scores_gemma":[0.9959452,0.000005259136,0.0009628192,0.00041152,0.000133204,0.0001185084,0.000003407557,0.00004612519,0.002374008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8629319,"threshold_uncertainty_score":0.9999771,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410204022","doi":"10.1109/taffc.2025.3567740","title":"Past, Present, and Future: A Survey of the Evolution of Affective Robotics for Well-Being","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robotics; Artificial intelligence; Psychology; Affective computing; Computer science; Cognitive science; Data science; Human–computer interaction; Cognitive psychology; Robot","retraction":null,"screen_n_in":null,"score":{"opus":0.01973837886126489,"gpt":0.3331362374628805,"spread":0.3133978586016156,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004179948,0.0001452888,0.0002718077,0.0001838843,0.0002852054,0.0000119704,0.0001181182,0.0001260465,0.0000201513],"category_scores_gemma":[0.00002536206,0.0001276412,0.000161404,0.0006267111,0.0001294047,0.00004533025,0.000004467736,0.0002667534,0.000001964572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000164433,"about_ca_system_score_gemma":0.00005448178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001191027,"about_ca_topic_score_gemma":0.0003171788,"domain_scores_codex":[0.9986187,0.0005566143,0.00026121,0.0002719798,0.0001089001,0.0001825562],"domain_scores_gemma":[0.9970354,0.002229523,0.0002016582,0.0002061412,0.0002993238,0.00002790432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00476174,0.004598137,0.08440781,0.0008950357,0.00526918,0.000002348723,0.03033332,0.1503648,0.008217477,0.07085195,0.008428823,0.6318694],"study_design_scores_gemma":[0.00293152,0.0005580505,0.9564146,0.0003344276,0.0003112748,0.000007044094,0.005300202,0.0199598,0.01201014,0.001458273,0.000413516,0.0003011048],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08046728,0.0000873762,0.9092218,0.0003892891,0.00412842,0.0008784104,0.00002347802,0.00003862188,0.00476532],"genre_scores_gemma":[0.9991345,0.000003348523,0.0002618296,0.00004910314,0.0001913324,0.00002742516,0.000001093802,0.0000149526,0.0003164042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9186673,"threshold_uncertainty_score":0.5205057,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2759514186","doi":"10.1109/taffc.2017.2757491","title":"Laughter and Tickles: Toward Novel Approaches for Emotion and Behavior Elicitation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Laughter; Perception; Sensation; Stimulus (psychology); Psychology; Cognitive psychology; Arousal; Social psychology; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.1214902950362882,"gpt":0.3500797924938081,"spread":0.2285894974575199,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002241772,0.000139758,0.0001447649,0.0001180253,0.0006963538,0.0001045962,0.00005367342,0.0001156019,0.00002793316],"category_scores_gemma":[0.00001593338,0.0001413153,0.00006300474,0.00003158228,0.0001111514,0.0001478365,0.000001911242,0.0001521128,0.00001093832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002966693,"about_ca_system_score_gemma":0.000006378971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003432774,"about_ca_topic_score_gemma":0.00002579358,"domain_scores_codex":[0.9992203,0.00005697165,0.0001384948,0.0003471024,0.00006906762,0.0001680839],"domain_scores_gemma":[0.9993954,0.0002002144,0.0001218768,0.0001553999,0.00006693384,0.00006017135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001013589,0.0005841605,0.0004701095,0.00007632063,0.00009145695,0.000001156028,0.006496297,0.0001004098,0.002267241,0.0009308064,0.00003378946,0.9888469],"study_design_scores_gemma":[0.01105879,0.001765265,0.9078762,0.0004078401,0.0008285089,0.0001862982,0.009300767,0.0517966,0.01440476,0.001138624,0.0002120194,0.001024319],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4430857,0.00001113916,0.5550268,0.0001043681,0.0005894878,0.0004530887,0.00001929384,0.00004340356,0.0006667298],"genre_scores_gemma":[0.996414,0.000005292059,0.003091767,0.00007084017,0.0001066776,0.00009870688,0.000007107205,0.00002216929,0.0001834348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9878226,"threshold_uncertainty_score":0.5762671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4301968314","doi":"10.1109/taffc.2022.3197456","title":"What Lies Beneath—A Survey of Affective Theory Use in Computational Models of Emotion","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotions and Moral Behavior","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Psychology; Affective computing; Computational model; Cognitive psychology; Computer science; Cognitive science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.07270830109485897,"gpt":0.3313310110959134,"spread":0.2586227100010544,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001185429,0.0001914737,0.0003669571,0.000564258,0.0002461715,0.00002272903,0.0001488994,0.00007682832,0.0002059698],"category_scores_gemma":[0.00001759193,0.0002169115,0.0001450845,0.0008729252,0.0001307458,0.0003440341,0.000008783413,0.0004521954,0.000003942705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964896,"about_ca_system_score_gemma":0.00005564413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001728731,"about_ca_topic_score_gemma":0.0005056983,"domain_scores_codex":[0.9967605,0.001912799,0.0004118524,0.0004039557,0.0002773689,0.0002334609],"domain_scores_gemma":[0.9970499,0.002188582,0.000237718,0.0002406921,0.0002414428,0.00004169296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003730262,0.001634312,0.006140758,0.00001720701,0.0001099748,0.000005965399,0.005994996,0.9345951,0.0002998119,0.003719953,0.00001651203,0.0470924],"study_design_scores_gemma":[0.002295819,0.001371918,0.9050139,0.0001830438,0.00008773631,0.00002861701,0.006958093,0.07877121,0.002074431,0.002842838,0.000001869878,0.0003705182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6054935,0.00004086523,0.3928982,0.00001076184,0.0009229436,0.0003728725,0.0001351769,0.00002968034,0.00009595224],"genre_scores_gemma":[0.9991987,0.000005272604,0.0005450239,0.00003449268,0.00001042317,0.00005113538,0.00003215901,0.00002995101,0.00009282466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8988732,"threshold_uncertainty_score":0.8845393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4372055039","doi":"10.1109/taffc.2023.3273201","title":"A Review of Tools and Methods for Detection, Analysis, and Prediction of Allostatic Load Due to Workplace Stress","year":2023,"lang":"en","type":"review","venue":"IEEE Transactions on Affective Computing","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Allostatic load; Allostasis; Stress (linguistics); Psychology; Computer science; Applied psychology; Cognitive psychology; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.05725290640934569,"gpt":0.3998596155610786,"spread":0.3426067091517329,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002218611,0.0002869747,0.002570341,0.0005277559,0.0001007564,0.00001457637,0.00005287728,0.0001743265,0.000006659534],"category_scores_gemma":[0.0004755599,0.0002536328,0.0005690733,0.001307418,0.00006338047,0.0000436799,0.000004362995,0.0003263941,0.000001069305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002058812,"about_ca_system_score_gemma":0.0002565478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004755317,"about_ca_topic_score_gemma":0.00003820812,"domain_scores_codex":[0.9976314,0.0006541311,0.0008909545,0.0005141381,0.0001234656,0.0001859394],"domain_scores_gemma":[0.9936843,0.005253353,0.000355772,0.0002886612,0.0002947751,0.0001231287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00002365372,0.00007272977,0.000001487585,0.1179455,0.001843946,5.917129e-7,0.0001272061,0.0001512747,0.00009287719,0.000001496389,0.000003105136,0.8797361],"study_design_scores_gemma":[0.003135588,0.003603574,0.000866185,0.7483392,0.08780257,0.0001444859,0.0002292124,0.04676855,0.003856217,0.00005785223,0.1041297,0.001066869],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0000534196,0.4132995,0.5836697,0.00002676123,0.0001152796,0.002507443,0.0002900469,0.00003360484,0.000004273825],"genre_scores_gemma":[0.001208828,0.988478,0.009860571,0.00005528949,0.00004731812,0.0002531931,0.00002171634,0.00004027722,0.00003477276],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.8786693,"threshold_uncertainty_score":0.9999916,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2922482968","doi":"10.1109/taffc.2021.3096831","title":"Improving Humanness of Virtual Agents and Users’ Cooperation Through Emotions","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Task (project management); Context (archaeology); Appraisal theory; Baseline (sea); Virtual agent; Ask price; Duration (music)","retraction":null,"screen_n_in":null,"score":{"opus":0.04613774495304748,"gpt":0.3549427646336476,"spread":0.3088050196806001,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001138785,0.0001351851,0.0001998641,0.00008546369,0.0004222778,0.00003981367,0.00005269303,0.0001016361,0.0005455242],"category_scores_gemma":[0.0000241555,0.0001543492,0.0001011999,0.0002752575,0.00007485782,0.0001488995,0.000002734381,0.0002833731,0.00004306563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007051903,"about_ca_system_score_gemma":0.00003378915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002072248,"about_ca_topic_score_gemma":0.0001272123,"domain_scores_codex":[0.998907,0.0002558379,0.0002298945,0.0003222841,0.0001119252,0.0001730251],"domain_scores_gemma":[0.9991167,0.0003639388,0.0001097878,0.0001579051,0.0002074253,0.0000442781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004992107,0.004627161,0.001760397,0.0001695662,0.001803402,0.0001005591,0.1228139,0.06798941,0.07673098,0.0313457,0.00172194,0.6904378],"study_design_scores_gemma":[0.0220386,0.005389198,0.2956879,0.001682136,0.001486807,0.0007720361,0.1542844,0.1315541,0.37879,0.001189143,0.003155712,0.003969994],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4370346,0.00001450116,0.5591501,0.00006174672,0.001666506,0.0001221383,0.000009215126,0.00005348127,0.001887731],"genre_scores_gemma":[0.9985059,0.000006048761,0.0004688675,0.0002415431,0.00009027636,0.00001064626,0.000004433952,0.00002094763,0.0006513469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6864678,"threshold_uncertainty_score":0.6294177,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107671794","doi":"10.1109/t-affc.2012.27","title":"Conative Dimensions of Machine Ethics: A Defense of Duty","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Duty; Morality; Sophistication; Argument (complex analysis); Epistemology; Philosophy; Sociology; Computer science; Social science","retraction":null,"screen_n_in":null,"score":{"opus":0.06585868937331733,"gpt":0.386098005894357,"spread":0.3202393165210397,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002444939,0.0001205494,0.0002750625,0.0001322306,0.0008402619,0.00001444357,0.0001226043,0.0001837334,0.00004587844],"category_scores_gemma":[0.000600552,0.0001072226,0.00017875,0.0003889819,0.0006378031,0.0002169149,0.000003045829,0.0007820344,0.000006978408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001113461,"about_ca_system_score_gemma":0.0002027464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003795434,"about_ca_topic_score_gemma":0.001387396,"domain_scores_codex":[0.9979635,0.0008925259,0.0002401951,0.000141756,0.0004378743,0.0003241334],"domain_scores_gemma":[0.9945177,0.004410411,0.0002070286,0.0001223752,0.0005867276,0.0001557481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001974961,0.002451672,0.003845296,0.0001676653,0.0006753643,0.000002962148,0.8033777,0.006345746,0.008248926,0.1330656,0.0001151532,0.04150639],"study_design_scores_gemma":[0.01171392,0.005106851,0.06278624,0.004609438,0.002058332,0.00002385389,0.2989101,0.01794358,0.5074608,0.07742574,0.006847673,0.005113479],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5930324,0.0002480593,0.379132,0.001596071,0.001588597,0.0006408424,0.00008416906,0.0001155113,0.0235623],"genre_scores_gemma":[0.9986897,0.00008022309,0.000892646,0.0001807339,0.00007702359,0.000002717063,5.567213e-7,0.00001323912,0.00006319778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5044677,"threshold_uncertainty_score":0.64627,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3181205532","doi":"10.1109/taffc.2021.3094894","title":"Discerning Affect From Touch and Gaze During Interaction With a Robot Pet","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Academy of Finland","keywords":"Gaze; Affect (linguistics); Robot; Human–computer interaction; Psychology; Human–robot interaction; Computer science; Cognitive psychology; Communication; Computer vision; Artificial intelligence; Cognitive science","retraction":null,"screen_n_in":null,"score":{"opus":0.02382521746774027,"gpt":0.3279653147384313,"spread":0.3041400972706911,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008388447,0.0002524073,0.0002936062,0.0001466438,0.0005239866,0.0001220688,0.0000672394,0.00007958458,0.0009985353],"category_scores_gemma":[0.00001642042,0.0002506305,0.0001260255,0.0002885858,0.00006052307,0.0001765101,0.000004173691,0.00068668,0.0001256837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849629,"about_ca_system_score_gemma":0.00002773345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004942482,"about_ca_topic_score_gemma":0.0006644656,"domain_scores_codex":[0.9983566,0.0003524716,0.0002041733,0.0006120027,0.000168278,0.0003064556],"domain_scores_gemma":[0.9985668,0.0008748537,0.0001249437,0.0002243035,0.00009930954,0.0001097851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006551845,0.004554403,0.02598365,0.0002662895,0.006259762,0.00297786,0.08633044,0.1154711,0.2211964,0.0008554017,0.0005162507,0.5290366],"study_design_scores_gemma":[0.01441387,0.002047038,0.6023976,0.002928885,0.000960239,0.003588668,0.06807685,0.02372422,0.2781349,0.0002143866,0.0006173578,0.002895945],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.661528,0.00003732669,0.3330305,0.0001149168,0.001576042,0.0001657754,0.00000929557,0.0001685157,0.003369617],"genre_scores_gemma":[0.9979869,0.000007813782,0.0008092629,0.000133394,0.0002344266,0.00002955239,0.000006389849,0.00004827593,0.0007440022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5764139,"threshold_uncertainty_score":0.9999946,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407123443","doi":"10.1109/taffc.2025.3537991","title":"SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Wavelet; Speech recognition; Artificial intelligence; Noise reduction; Filter (signal processing); Pattern recognition (psychology); Thresholding; Deep learning; Feature (linguistics); Convolutional neural network; Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.0184349270498372,"gpt":0.2657496826307175,"spread":0.2473147555808803,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006700397,0.0002405382,0.0002413008,0.0002842692,0.00123184,0.0002188791,0.0002656279,0.0001372551,0.000008246726],"category_scores_gemma":[0.00004112826,0.0002649949,0.0001856292,0.000926928,0.00004159556,0.0004007081,0.000006727239,0.0004687588,0.00002647053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002386158,"about_ca_system_score_gemma":0.0000879213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001788122,"about_ca_topic_score_gemma":0.00001583212,"domain_scores_codex":[0.9981542,0.0002237787,0.0002966877,0.0006220635,0.0002025481,0.0005007452],"domain_scores_gemma":[0.9985151,0.0008136131,0.0001609886,0.000188071,0.0002539461,0.00006825611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000421749,0.00009434795,0.00003498009,0.00004652274,0.00004507187,0.000002198139,0.0002079266,0.1188954,0.004682986,0.00004760716,0.0001374255,0.8757634],"study_design_scores_gemma":[0.0009733468,0.0003022588,0.0006089804,0.0004697583,0.00003516137,0.00001204044,0.0000703371,0.7366351,0.2580634,0.00232662,0.0002113,0.0002916689],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04018861,0.00005287022,0.9566475,0.000198167,0.001225092,0.0005739816,0.00000382927,0.0005355434,0.0005743825],"genre_scores_gemma":[0.8397709,0.000006122573,0.159591,0.0002325924,0.0001953212,0.0000307629,0.000006940782,0.00001652785,0.0001498677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8754717,"threshold_uncertainty_score":0.9999802,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399527282","doi":"10.1109/taffc.2024.3412152","title":"Controllable Multi-Speaker Emotional Speech Synthesis With an Emotion Representation of High Generalization Capability","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Generalization; Speech recognition; Emotion recognition; Representation (politics); Speaker recognition; Computer science; Emotion classification; Psychology; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02894511557105801,"gpt":0.2795308599183586,"spread":0.2505857443473006,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005107594,0.0002190313,0.0003021118,0.0003768333,0.0002545738,0.0001893491,0.0002188328,0.00009773653,0.0001056407],"category_scores_gemma":[0.00004870035,0.0001967344,0.0001424949,0.0009886007,0.00008219524,0.0007654346,0.00000357416,0.0001918917,0.0000343946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001851404,"about_ca_system_score_gemma":0.00008397092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002683714,"about_ca_topic_score_gemma":0.0001223042,"domain_scores_codex":[0.9977871,0.0005216506,0.0003410944,0.0007011369,0.0004202938,0.0002287221],"domain_scores_gemma":[0.9981914,0.0008471977,0.0001172314,0.0003970928,0.0003544259,0.00009263898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001125326,0.0008635604,0.0002375979,0.0001228505,0.0002529285,0.00002426976,0.001009105,0.09840456,0.01177478,0.001575596,0.00002623488,0.885596],"study_design_scores_gemma":[0.0004510899,0.0002100698,0.005437187,0.000209621,0.00005280772,0.00004658262,0.00009137305,0.7537588,0.2391758,0.00035777,0.000006073403,0.0002027747],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3002662,0.00001159525,0.6983427,0.0001204765,0.0004844794,0.0003198757,0.00001880265,0.0003076401,0.0001281411],"genre_scores_gemma":[0.8884513,0.000006428077,0.1113368,0.00004089041,0.00006289653,0.00002530721,0.000006025202,0.00002197225,0.00004828008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8853932,"threshold_uncertainty_score":0.8022597,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413277779","doi":"10.1109/taffc.2025.3599859","title":"Leveraging Eye Movement for Instructing Robust Video-Based Facial Expression Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Hubei Key Laboratory of Intelligent Geo-Information Processing; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Facial expression; Computer science; Facial expression recognition; Computer vision; Eye movement; Artificial intelligence; Emotion recognition; Expression (computer science); Speech recognition; Human–computer interaction; Facial recognition system; Feature extraction","retraction":null,"screen_n_in":null,"score":{"opus":0.02881493789973664,"gpt":0.2737631537182268,"spread":0.2449482158184902,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004130513,0.0002488339,0.0002548956,0.0005199657,0.0009110286,0.0001297297,0.0003852332,0.0001332328,0.000004003178],"category_scores_gemma":[0.00004704811,0.0002636597,0.0001775384,0.0006201491,0.00006035443,0.0002051004,0.00001003609,0.0004020116,0.000007621618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002667628,"about_ca_system_score_gemma":0.00008198287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001561808,"about_ca_topic_score_gemma":0.000006244941,"domain_scores_codex":[0.9982797,0.0001364675,0.0003010622,0.00071161,0.0001765373,0.0003946406],"domain_scores_gemma":[0.9986632,0.0006161085,0.0001505447,0.0003300556,0.0001916433,0.00004844723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004740819,0.0002123918,0.0002048082,0.00006941373,0.00004940013,0.000002863933,0.0002314639,0.1270312,0.02353936,0.0002644762,0.00005614995,0.8482911],"study_design_scores_gemma":[0.001462966,0.0001793453,0.001436514,0.0005490541,0.00002510877,0.000001284994,0.0001302677,0.4636919,0.529653,0.002516628,0.00006306125,0.0002908536],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1716171,0.000009026844,0.8252823,0.0003548564,0.001317792,0.0004702267,0.000007423577,0.0006580534,0.0002832614],"genre_scores_gemma":[0.8924426,7.941715e-7,0.1070329,0.0003575611,0.00004142911,0.00007617528,0.000002581658,0.00001411147,0.00003185048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8480002,"threshold_uncertainty_score":0.9999816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400447955","doi":"10.1109/taffc.2024.3424882","title":"Exploring the Boundaries of Semi-Supervised Facial Expression Recognition Using In-Distribution, Out-of-Distribution, and Unconstrained Data","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Facial expression; Facial expression recognition; Distribution (mathematics); Pattern recognition (psychology); Emotion recognition; Artificial intelligence; Expression (computer science); Computer science; Speech recognition; Facial recognition system; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1173636523165885,"gpt":0.3011579387551304,"spread":0.183794286438542,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006611423,0.000182636,0.0002350105,0.0001478984,0.0004579577,0.0002045261,0.000341955,0.00007003442,0.0000056439],"category_scores_gemma":[0.0000615297,0.0001515565,0.00007189093,0.0006474755,0.0002486738,0.001108897,0.00003244916,0.0003256866,0.000003697115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008759939,"about_ca_system_score_gemma":0.0001518482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008654795,"about_ca_topic_score_gemma":0.00003221842,"domain_scores_codex":[0.9983575,0.0002489242,0.000410434,0.0004972597,0.0002622119,0.0002236957],"domain_scores_gemma":[0.9986993,0.0005935918,0.0001083952,0.0003826128,0.0001667325,0.00004937046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000108455,0.0002766008,0.0001188725,0.0004070586,0.0001057144,0.00001368127,0.007332036,0.01022507,0.07181846,0.00028323,0.0001770898,0.9091337],"study_design_scores_gemma":[0.0006600411,0.0001383547,0.0007266979,0.002017384,0.00005379057,0.00002269655,0.001041017,0.7158352,0.277759,0.001207496,0.0002408216,0.0002974674],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3544237,0.00006180504,0.6439095,0.00007359455,0.0008895107,0.0002063755,0.0003479724,0.00007528595,0.00001225999],"genre_scores_gemma":[0.9968323,0.00003613542,0.002932964,0.00001219008,0.00004826376,0.00001296967,0.0001139443,0.000009826455,0.000001474923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9088362,"threshold_uncertainty_score":0.6180293,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409494582","doi":"10.1109/taffc.2025.3562027","title":"Partial Label Learning for Emotion Recognition From EEG","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Electroencephalography; Emotion recognition; Psychology; Cognitive psychology; Emotion classification; Computer science; Artificial intelligence; Speech recognition; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.04141856165419956,"gpt":0.3047044322839445,"spread":0.263285870629745,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001945314,0.0001787519,0.0001888187,0.0001873919,0.0006477781,0.0001091316,0.0001365261,0.00009000317,0.00002323374],"category_scores_gemma":[0.0001047906,0.0001861181,0.0001234423,0.0003233854,0.00005533018,0.0001567192,0.00000329844,0.0003806841,0.000046867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000962519,"about_ca_system_score_gemma":0.00002729762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003238225,"about_ca_topic_score_gemma":0.00001022239,"domain_scores_codex":[0.9985369,0.0002972663,0.0002167057,0.0005529569,0.0001284069,0.000267758],"domain_scores_gemma":[0.9975582,0.002112733,0.00009158267,0.0001224509,0.00007206291,0.00004298248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001754002,0.0003056118,0.00003353798,0.00003487917,0.00004159308,0.000002331984,0.0007279263,0.04048988,0.2952053,0.00006566625,0.0001306381,0.6627872],"study_design_scores_gemma":[0.0009065645,0.0002473207,0.0002465757,0.0002087203,0.00003237862,0.000002025657,0.00009455209,0.3071825,0.6899744,0.0007098592,0.0002405814,0.0001545116],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4358659,0.000005711421,0.5619078,0.0001115709,0.001259038,0.000283325,0.00001744963,0.0001947264,0.0003545513],"genre_scores_gemma":[0.9973712,0.000004499754,0.001822064,0.0004251425,0.0001109089,0.00003224377,0.000004826368,0.00001821435,0.0002109209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6626327,"threshold_uncertainty_score":0.7589675,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405219125","doi":"10.1109/taffc.2024.3514933","title":"Nonverbal Leadership in Joint Full-Body Improvisation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Team Dynamics and Performance","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Nonverbal communication; Improvisation; Psychology; Joint (building); Body language; Cognitive psychology; Communication; Social psychology; Visual arts; Art; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06245129761801876,"gpt":0.3094611238296057,"spread":0.2470098262115869,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004363654,0.0001759486,0.0001655054,0.0003601515,0.0001236259,0.00006182848,0.00008609703,0.000124892,0.0002191095],"category_scores_gemma":[0.000003262182,0.0001805234,0.0001187647,0.0004571007,0.0000482366,0.0001100922,0.000001212297,0.0006462968,0.0004389848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002597314,"about_ca_system_score_gemma":0.00003909823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001892453,"about_ca_topic_score_gemma":0.0001559189,"domain_scores_codex":[0.9986925,0.0001321106,0.0002302695,0.000421677,0.0001261508,0.0003972747],"domain_scores_gemma":[0.9994582,0.0002424848,0.00003796469,0.0001810713,0.00002523617,0.00005502124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003104927,0.0007189827,0.0007666067,0.0003896003,0.0002792033,0.0001291936,0.02356448,0.04111136,0.01269745,0.004598339,0.000347307,0.915087],"study_design_scores_gemma":[0.001052741,0.0007171649,0.0330385,0.0004755632,0.00004413979,0.00008624301,0.001942934,0.9592717,0.002343149,0.0002041376,0.0003373676,0.0004863181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4283187,0.00007619929,0.5658953,0.0002172397,0.003059234,0.0002429781,0.000009340205,0.0001962535,0.001984774],"genre_scores_gemma":[0.9990374,0.000002914157,0.0001689692,0.0002109733,0.0001555937,0.00002761557,0.000002474515,0.00003259429,0.0003615011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9181604,"threshold_uncertainty_score":0.7361532,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405304022","doi":"10.1109/taffc.2024.3516822","title":"FedAR: Federated Artificial Resampling for Imbalanced Facial Emotion Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Emotion recognition; Resampling; Computer science; Artificial intelligence; Facial expression; Affective computing; Emotion detection; Speech recognition; Pattern recognition (psychology); Emotion classification; Facial recognition system; Psychology; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.04156447279572322,"gpt":0.2971260507360293,"spread":0.2555615779403061,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004390543,0.0002228842,0.0002335965,0.0004302988,0.0008064677,0.0006945973,0.0001592217,0.0001150243,0.00002633325],"category_scores_gemma":[0.00002861695,0.0002311028,0.0003158629,0.0009260127,0.00003286038,0.0004169094,0.00000307829,0.0003201756,0.0001796416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001871362,"about_ca_system_score_gemma":0.00007429726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001761711,"about_ca_topic_score_gemma":0.00003109868,"domain_scores_codex":[0.9983055,0.0001475968,0.0003105001,0.0006692569,0.000224351,0.0003428235],"domain_scores_gemma":[0.9989042,0.0005660882,0.0000686112,0.0001487431,0.0002237157,0.00008865867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003239614,0.0001047654,0.000001343494,0.00005245724,0.0001068777,0.000005073407,0.0004202606,0.01572562,0.01275214,0.000210579,0.00006858377,0.9705199],"study_design_scores_gemma":[0.0003128771,0.0001851183,0.00003866727,0.0002430766,0.00005158303,0.00001247511,0.0001201525,0.9092629,0.08697473,0.002385917,0.0001236316,0.0002888508],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04877101,0.00002271563,0.9471866,0.0003905476,0.002002083,0.0004423859,0.00004630081,0.0009103522,0.0002279938],"genre_scores_gemma":[0.9858347,0.000008744318,0.01365277,0.0001295373,0.0002221856,0.00004476299,0.0000212898,0.00002444304,0.00006157252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9702311,"threshold_uncertainty_score":0.9424098,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7091447115","doi":"10.1109/taffc.2025.3619161","title":"Preparing the Heart for Duty: Virtual Reality Biofeedback in an Arousing Action Game Improves in-Action Voluntary Heart Rate Variability Control in Experienced Police","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Biofeedback; Heart rate variability; Virtual reality; Arousal; Action (physics); Control (management); Neurofeedback; Intervention (counseling)","retraction":null,"screen_n_in":null,"score":{"opus":0.02848682961929271,"gpt":0.3423115843288962,"spread":0.3138247547096035,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003145481,0.0002695223,0.0006149379,0.0004282869,0.0002630742,0.00006276349,0.00009336999,0.0002050316,0.00000627975],"category_scores_gemma":[0.0001593015,0.0002503874,0.0001723727,0.0007897114,0.0001387891,0.0003722548,0.000004714052,0.000784668,0.000001896589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297628,"about_ca_system_score_gemma":0.0003582431,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006779836,"about_ca_topic_score_gemma":0.009140966,"domain_scores_codex":[0.9967601,0.00119868,0.0006779018,0.0007486235,0.0001244611,0.0004902308],"domain_scores_gemma":[0.9969006,0.002377458,0.00009759753,0.0004347463,0.0001037695,0.00008585487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002696008,0.00159917,0.01304267,0.0002072783,0.0000747167,0.000001481856,0.00661413,0.1335216,0.7561263,0.00005430647,0.000005906226,0.08605637],"study_design_scores_gemma":[0.004192224,0.0005049775,0.3693112,0.0002901834,0.0000451892,0.00000660299,0.002151866,0.6029881,0.02005591,0.0001279512,0.0001269984,0.0001987474],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7082188,0.000004227889,0.2880189,0.0009807611,0.0005594879,0.002096042,0.000009261347,0.00007849629,0.00003413954],"genre_scores_gemma":[0.9984878,0.000001950186,0.0002271025,0.0008887052,0.0001055591,0.0002474785,0.000003753023,0.00001984851,0.00001774658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7360704,"threshold_uncertainty_score":0.9999948,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415594183","doi":"10.1109/taffc.2025.3625612","title":"Modeling Multimodal Depression Diagnosis From the Perspective of Local Depressive Representation","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Focus (optics); Affective computing; Mood; Consistency (knowledge bases); Perspective (graphical); Representation (politics); Perception; Major depressive disorder; Limiting","retraction":null,"screen_n_in":null,"score":{"opus":0.02225362969908221,"gpt":0.3311635817890787,"spread":0.3089099520899964,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005268178,0.0005942322,0.0007606557,0.0004189408,0.001151331,0.0001006405,0.0004931079,0.0004835432,0.0003420783],"category_scores_gemma":[0.0001435035,0.0005124235,0.000702677,0.001129055,0.0004179958,0.0001818939,0.00001899355,0.001368438,0.0000525045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005128576,"about_ca_system_score_gemma":0.0001721967,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01644752,"about_ca_topic_score_gemma":0.0009388188,"domain_scores_codex":[0.9943637,0.002227394,0.0008661289,0.00146173,0.0005025876,0.000578411],"domain_scores_gemma":[0.9923684,0.005523638,0.0004235924,0.0008929536,0.0006795507,0.0001118444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001542328,0.00182361,0.001583542,0.0000573879,0.002113243,0.00002305506,0.02659523,0.5823824,0.001680615,0.0006566883,0.00009367899,0.3814482],"study_design_scores_gemma":[0.004735445,0.0003109975,0.00848063,0.001833369,0.00181205,0.000008378272,0.07245762,0.7861403,0.1200979,0.003540698,0.000007300922,0.0005753834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.311089,0.002324168,0.6793559,0.0001813129,0.002633095,0.001155935,0.0001594466,0.00009106033,0.003010121],"genre_scores_gemma":[0.9983473,0.0002083413,0.0004767284,0.0003090263,0.0002858012,0.000217961,0.00001632574,0.00006060886,0.00007790541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6872583,"threshold_uncertainty_score":0.9997327,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411551248","doi":"10.1109/taffc.2025.3582198","title":"Multimodal Framework for Therapeutic Consultations","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Counseling, Therapy, and Family Dynamics","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Michael's Hospital; Toronto Metropolitan University; Pediatric Oncology Group","funders":"","keywords":"Psychology; Multimodal therapy; Computer science; Psychotherapist; Artificial intelligence; Human–computer interaction; Cognitive psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02145036261429461,"gpt":0.3486437458122792,"spread":0.3271933831979846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002602131,0.0002267491,0.0002498564,0.000263918,0.0006091496,0.00004754173,0.0001675916,0.0002087304,0.00007142779],"category_scores_gemma":[0.00002074238,0.0002401822,0.000226332,0.0004790745,0.0001065781,0.00003965552,8.925655e-7,0.0004657157,0.00004838529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207365,"about_ca_system_score_gemma":0.00005888491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005203466,"about_ca_topic_score_gemma":0.00004131934,"domain_scores_codex":[0.9986737,0.0001619732,0.0002579951,0.0004493246,0.00009950467,0.00035744],"domain_scores_gemma":[0.9958178,0.003543959,0.00008599056,0.000306985,0.0001949412,0.00005034556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009979922,0.001684502,0.0004348215,0.00007770398,0.001697444,0.00000466717,0.04545062,0.06297728,0.001314975,0.08828174,0.0005207019,0.7965575],"study_design_scores_gemma":[0.02126149,0.002818238,0.04426487,0.001450828,0.001595914,0.00004065461,0.0745099,0.6897796,0.001361208,0.1491971,0.0104938,0.003226319],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.105259,0.0001525034,0.8883379,0.0001964822,0.003933439,0.0008738688,0.00005029516,0.0002573012,0.0009393093],"genre_scores_gemma":[0.9836717,0.00001966815,0.01431914,0.00108275,0.00009443693,0.0001508437,0.000004074061,0.00003504434,0.0006223505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8784127,"threshold_uncertainty_score":0.9794344,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415748352","doi":"10.1109/taffc.2025.3627534","title":"Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Bell (Canada); HEC Montréal","funders":"","keywords":"Exploit; Domain (mathematical analysis); Forgetting; Focus (optics); Similarity (geometry); Facial expression; Adaptation (eye); Face (sociological concept)","retraction":null,"screen_n_in":null,"score":{"opus":0.05386957407189527,"gpt":0.3495630202703141,"spread":0.2956934461984188,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003198593,0.0002329743,0.0002352721,0.00036095,0.0006364387,0.00004583032,0.00008489942,0.0002131694,0.0001881224],"category_scores_gemma":[0.00003124121,0.0002407841,0.0002434573,0.0003166501,0.00009065735,0.0001054703,0.000001684723,0.0003100415,0.00006642115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145183,"about_ca_system_score_gemma":0.00003995466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001934223,"about_ca_topic_score_gemma":0.0000196495,"domain_scores_codex":[0.9983059,0.0004218848,0.0002888953,0.000534195,0.0001402916,0.000308833],"domain_scores_gemma":[0.9988155,0.000562867,0.0001710807,0.0001438484,0.000239859,0.000066787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001316378,0.001276258,0.00003131647,0.0000937654,0.0002422181,0.000005271767,0.01054532,0.002489538,0.01109699,0.0001954295,0.0003070186,0.9724005],"study_design_scores_gemma":[0.09056752,0.005467063,0.01105059,0.007329083,0.001511952,0.0002053139,0.08784117,0.3588032,0.4128616,0.008848283,0.01109884,0.004415361],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1258166,0.00005265281,0.8689219,0.0001000739,0.002158615,0.001519365,0.00006041527,0.0002835373,0.001086841],"genre_scores_gemma":[0.9824222,0.000002380137,0.01496353,0.0002555362,0.0001307073,0.00043004,0.00005641791,0.00003315879,0.001706016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9679852,"threshold_uncertainty_score":0.9818892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7105855072","doi":"10.1109/taffc.2025.3634148","title":"Mind AI's Mind: A Clinically Aligned Explainable AI Pipeline for Depression Diagnosis via Large Language Models","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Pipeline (software); Harm; Flagging; Skepticism; Field (mathematics); Dual (grammatical number)","retraction":null,"screen_n_in":null,"score":{"opus":0.01972609599373583,"gpt":0.3559604921077318,"spread":0.336234396113996,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003195208,0.0009710329,0.001265513,0.0009792527,0.002769994,0.0005050225,0.001437029,0.0007426112,0.0001048033],"category_scores_gemma":[0.0003744709,0.001056441,0.0009109016,0.001815579,0.0001505743,0.0008298666,0.00007750493,0.001875688,0.00005983215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006544741,"about_ca_system_score_gemma":0.0006324662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005823282,"about_ca_topic_score_gemma":0.000367338,"domain_scores_codex":[0.9912661,0.001847956,0.001765455,0.002624999,0.0007306254,0.001764864],"domain_scores_gemma":[0.9889345,0.007370309,0.0006690949,0.001574776,0.0009964067,0.0004549258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004174924,0.001439775,0.0007460513,0.0005499486,0.0001826198,0.0000359496,0.006726669,0.3457278,0.0001871316,0.0001245022,0.000466856,0.6433952],"study_design_scores_gemma":[0.003667675,0.0009581922,0.0002520054,0.002140377,0.0002442792,0.00001817589,0.0004765822,0.9769445,0.01306853,0.0007654036,0.0006258436,0.0008384273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02663462,0.0007543097,0.9604609,0.003362153,0.004374182,0.003620971,0.0001527985,0.0002875674,0.0003524891],"genre_scores_gemma":[0.9624573,0.00004774349,0.03249531,0.003322626,0.0003439999,0.0004990451,0.00001492087,0.00009678473,0.0007222658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9358227,"threshold_uncertainty_score":0.9991886,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}