{"id":"W1977606836","doi":"10.1167/10.6.22","title":"Frames of reference for biological motion and face perception","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Queen's University","funders":"York University; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Biological motion; Reference frame; Perception; Stimulus (psychology); Frame of reference; Computer vision; Artificial intelligence; Observer (physics); Visual perception; Computer science; Communication; Rendering (computer graphics); Psychology; Motion (physics); Cognitive psychology; Frame (networking); Physics; Neuroscience; Classical mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002844478,0.00004897526,0.0001041359,0.00007721949,0.00004881881,0.00001825024,0.00005852443,0.00009855538,0.0001829565],"category_scores_gemma":[0.0005263273,0.00003351293,0.00004682273,0.00004941387,0.0000661364,0.0001904679,0.00001231567,0.0001848105,0.000007254585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006549121,"about_ca_system_score_gemma":0.000007552985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001080799,"about_ca_topic_score_gemma":0.000001174035,"domain_scores_codex":[0.9994617,0.00004936338,0.0002126063,0.00008862581,0.000125235,0.00006244813],"domain_scores_gemma":[0.9994749,0.0001325903,0.0001979928,0.00004648171,0.0001026375,0.00004540398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007234154,0.00004570526,0.0003320577,0.000008384893,4.461388e-7,2.47544e-7,0.000122948,0.000002413291,0.9192317,0.0001358634,0.00009364613,0.07995427],"study_design_scores_gemma":[0.001993737,0.003769371,0.7041026,0.0001596036,0.00002628145,0.0003278588,0.0008603139,0.005178674,0.2682827,0.008307741,0.006751457,0.0002396575],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926437,0.000006914285,0.006582648,0.0003146911,0.000227524,0.00007879933,0.000007957009,0.00000563593,0.0001321436],"genre_scores_gemma":[0.997134,0.0002654705,0.00242114,0.00008605012,0.00006568801,7.17123e-7,0.000001357561,0.000003000966,0.00002261886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7037706,"threshold_uncertainty_score":0.2003245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09194223531793766,"score_gpt":0.3655756238165302,"score_spread":0.2736333884985926,"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."}}