{"id":"W2989688566","doi":"10.1109/smc.2019.8914646","title":"Affective Computing Out-of-The-Lab: The Cost of Low Cost","year":2019,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Biofeedback; Computer science; Action (physics); Artificial intelligence; Human–computer interaction; Eye tracking; Obstacle; Health care; Machine learning; Cognitive psychology; Physical medicine and rehabilitation; Simulation; Psychology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003590715,0.0009064846,0.0007412473,0.0009351737,0.0006902379,0.004237722,0.002600953,0.001347231,0.03980475],"category_scores_gemma":[0.0109298,0.0004530203,0.0007581782,0.0009870906,0.001011497,0.003033741,0.002425664,0.001388521,0.0166861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009043494,"about_ca_system_score_gemma":0.0008261061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00123196,"about_ca_topic_score_gemma":0.001636901,"domain_scores_codex":[0.9973116,0.0006516412,0.00008000763,0.0003335126,0.001391291,0.0002320075],"domain_scores_gemma":[0.9916648,0.003252194,0.0003964189,0.001631333,0.002125271,0.0009299339],"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":[0.001276292,0.0002722935,0.003043641,0.001458612,0.0001222116,0.0005308087,0.0006058497,0.001139317,0.05500451,0.01198238,0.06290322,0.8616608],"study_design_scores_gemma":[0.0002794956,0.005108251,0.03948202,0.001661698,0.0005800896,0.006756815,0.00247245,0.01732891,0.06907979,0.0321188,0.824699,0.0004326766],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1490444,0.0244675,0.6122621,0.03729683,0.007403049,0.001422784,0.003172476,0.01512224,0.1498086],"genre_scores_gemma":[0.503453,0.01191968,0.3613826,0.01030527,0.00168674,0.001628605,0.003126575,0.003406354,0.1030912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03980475,"threshold_uncertainty_score":0.1331602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02978632477857492,"score_gpt":0.3200521802912669,"score_spread":0.290265855512692,"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."}}