{"id":"W2468573874","doi":"10.1117/12.2241108","title":"Effects of training set selection on pain recognition via facial expressions","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Northern British Columbia","keywords":"Support vector machine; Computer science; Outlier; Artificial intelligence; Pattern recognition (psychology); Facial expression; Data set","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004879222,0.001180933,0.001178838,0.0005268147,0.0006440532,0.001017951,0.0006771433,0.0008947903,0.001078147],"category_scores_gemma":[0.02784202,0.0004187717,0.0006602555,0.000502725,0.000651633,0.001022436,0.001020457,0.0009625835,0.0006255998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003741588,"about_ca_system_score_gemma":0.000371327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001591918,"about_ca_topic_score_gemma":0.001417322,"domain_scores_codex":[0.9956188,0.002151717,0.0004771377,0.0006207768,0.0008867972,0.0002447812],"domain_scores_gemma":[0.9814854,0.01406043,0.0009829928,0.001859707,0.001357966,0.0002537082],"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":[0.006677331,0.001219518,0.0369965,0.0004765926,0.0004504227,0.0005171894,0.0006741441,0.07389603,0.1601581,0.0002509803,0.002290898,0.7163923],"study_design_scores_gemma":[0.0003561041,0.00667432,0.1260184,0.0001803199,0.0007508818,0.001688842,0.0007265029,0.530687,0.327934,0.0008965084,0.003874747,0.0002124173],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392376,0.001500307,0.05505262,0.0003320053,0.0001912442,0.0002486054,0.0002619266,0.001397119,0.001778586],"genre_scores_gemma":[0.9640545,0.0004450298,0.03287632,0.000195188,0.00005785967,0.0001695819,0.0008336493,0.0002921045,0.001075938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004879222,"threshold_uncertainty_score":0.0258041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352533891235453,"score_gpt":0.2644491312807254,"score_spread":0.2409237923683709,"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."}}