{"id":"W2951902026","doi":"10.1007/978-3-030-22514-8_5","title":"Two-Layer Feature Selection Algorithm for Recognizing Human Emotions from 3D Motion Analysis","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sadness; Computer science; Artificial intelligence; Emotion classification; Motion (physics); Feature selection; Biometrics; Feature (linguistics); Anger; Happiness; Selection (genetic algorithm); Filter (signal processing); Pattern recognition (psychology); Machine learning; Computer vision; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005731246,0.0005240391,0.0006235647,0.001906481,0.0006892378,0.0008680316,0.001291035,0.0004440625,0.0001311862],"category_scores_gemma":[0.00003903885,0.0005297249,0.0003891768,0.001362165,0.0001557143,0.001055533,0.0003323574,0.0008154343,0.0001350388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004066337,"about_ca_system_score_gemma":0.0002815006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001272433,"about_ca_topic_score_gemma":0.0004043846,"domain_scores_codex":[0.996293,0.00005421128,0.000493738,0.001859604,0.0007409473,0.0005584697],"domain_scores_gemma":[0.9976578,0.0003466351,0.0004497529,0.0008451358,0.0005547462,0.0001458889],"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":[0.000002646488,0.00004092456,0.00004768265,0.00001338219,0.0001443071,0.000006378592,0.0002827618,0.0131619,0.000683749,0.001957612,0.00004824719,0.9836104],"study_design_scores_gemma":[0.000484161,0.000176607,0.0003465046,0.0001748695,0.000205019,0.00001941164,5.35201e-7,0.9062527,0.002334078,0.08845366,0.0008558628,0.0006965405],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003368647,0.00007248072,0.9946099,0.0003110543,0.001688116,0.0006715778,0.00004940233,0.0002502472,0.00201034],"genre_scores_gemma":[0.0751102,0.00002075156,0.9191335,0.001354767,0.001642213,0.00003235515,0.0004171682,0.00005755236,0.00223152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9829139,"threshold_uncertainty_score":0.9997154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02707550304794408,"score_gpt":0.2749985506805429,"score_spread":0.2479230476325988,"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."}}