{"id":"W2467503907","doi":"10.1145/2948910.2948936","title":"Automatic Affect Classification of Human Motion Capture Sequences in the Valence-Arousal Model","year":2016,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Categorical variable; Hidden Markov model; Arousal; Motion capture; Artificial intelligence; Computer science; Valence (chemistry); Affect (linguistics); Motion (physics); Perception; Machine learning; Pattern recognition (psychology); Speech recognition; Psychology; Communication","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004081451,0.00007048142,0.00008732323,0.00009046861,0.00003644615,0.000006927792,0.0001243711,0.00009643294,0.0006219079],"category_scores_gemma":[0.00002320436,0.00003538534,0.00004110384,0.0001116318,0.00007287988,0.00009551205,0.000005520444,0.0000644301,0.0001011472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002298821,"about_ca_system_score_gemma":0.00001106546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006686655,"about_ca_topic_score_gemma":0.000292831,"domain_scores_codex":[0.9991563,0.0002399553,0.0002054573,0.0001533353,0.0001330924,0.0001118667],"domain_scores_gemma":[0.9995713,0.00008519948,0.00009870148,0.0001919366,0.00003636611,0.0000165562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003939427,0.001309837,0.03809652,0.0001600741,0.00006539999,0.000007880873,0.01974977,0.0000557325,0.2577892,0.4310272,0.01456277,0.2371363],"study_design_scores_gemma":[0.002605109,0.0003953109,0.8940837,0.000334068,0.00007545995,0.00005031143,0.008494711,0.02202023,0.003856116,0.06761014,0.00006428541,0.000410494],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9393765,0.00001595672,0.01530305,0.001301224,0.0001645145,0.000268654,0.000006416082,0.00005793713,0.04350572],"genre_scores_gemma":[0.9979843,0.000005128025,0.0001460577,0.0001373811,0.00002607811,0.00004564257,0.00001141234,0.000005522391,0.00163842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8559873,"threshold_uncertainty_score":0.6809455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08640527851338783,"score_gpt":0.3566219399271366,"score_spread":0.2702166614137488,"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."}}