{"id":"W1996089789","doi":"10.1109/icpr.2014.784","title":"Automatic Pain Recognition from Video and Biomedical Signals","year":2014,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":168,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; University of Northern British Columbia","keywords":"Modalities; Computer science; Task (project management); Skin conductance; Facial expression; Electromyography; Relevance (law); Artificial intelligence; Pain assessment; Physical medicine and rehabilitation; Speech recognition; Machine learning; Pain management; Medicine; Physical therapy; Engineering; Biomedical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006804861,0.00007696458,0.0001109664,0.00007844251,0.00004124026,0.00002049718,0.00003621099,0.0001220897,0.02818769],"category_scores_gemma":[0.0002649351,0.00006439001,0.00002881008,0.00006679673,0.00005510145,0.00004560338,0.00001135718,0.00007537457,0.002087036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005950829,"about_ca_system_score_gemma":0.00000436572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001205735,"about_ca_topic_score_gemma":0.00002053206,"domain_scores_codex":[0.9989561,0.0004337603,0.0001832994,0.0002026996,0.00009367619,0.0001305052],"domain_scores_gemma":[0.9990687,0.0006484712,0.00004567607,0.0001031782,0.00002541437,0.0001084971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006524099,0.0000982823,0.0001977464,0.000008022631,0.00002569451,0.000001715172,0.0003373704,8.573219e-9,0.001306468,0.0002625924,0.02479295,0.9729626],"study_design_scores_gemma":[0.01729833,0.003927585,0.2279199,0.0009569477,0.0005144147,0.0001654696,0.007482076,0.05521959,0.007633507,0.4101779,0.2660725,0.002631693],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8661492,0.00005120913,0.07882141,0.001980066,0.0007631003,0.0001665322,0.00001991503,0.0003137587,0.05173481],"genre_scores_gemma":[0.992818,0.000006371167,0.002789431,0.00318079,0.0002511913,0.00002572346,0.0002015222,0.00001150663,0.0007154673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.970331,"threshold_uncertainty_score":0.9986899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03240428142878837,"score_gpt":0.2958055535562858,"score_spread":0.2634012721274974,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}