{"id":"W2007147050","doi":"10.3758/bf03193746","title":"Kinematic cues for person identification from biological motion","year":2007,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Kinematics; Biological motion; Normalization (sociology); Amplitude; Harmonics; Viewpoints; Artificial intelligence; Computer science; Generalization; Fourier analysis; Point (geometry); Computer vision; Mathematics; Communication; Fourier transform; Psychology; Pattern recognition (psychology); Motion (physics); Acoustics; Mathematical analysis; Physics; Optics; Geometry; Classical mechanics","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":[0.0001358922,0.0001167077,0.0001295625,0.00007876151,0.00006776073,0.00004416125,0.00006863812,0.00008475866,0.0003172288],"category_scores_gemma":[0.00001369877,0.0001127431,0.0001458281,0.0001725566,0.00002110966,0.0001305724,0.000002523047,0.00007322584,0.000422993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004822623,"about_ca_system_score_gemma":0.000001361245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009527472,"about_ca_topic_score_gemma":0.00000961836,"domain_scores_codex":[0.9993274,0.00001260252,0.0002173197,0.0001838228,0.0001085806,0.0001503375],"domain_scores_gemma":[0.9996529,0.00004367649,0.00004342037,0.000139042,0.00006893722,0.00005203681],"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":[0.00002466533,0.0001372366,0.0007879201,0.00005754526,0.00007719373,3.064162e-7,0.001451038,0.001210599,0.67188,0.0001935464,0.002301932,0.321878],"study_design_scores_gemma":[0.001782564,0.0001106828,0.6900886,0.0001013718,0.0002840855,0.000002285346,0.006916516,0.262381,0.008465978,0.02603212,0.002779181,0.001055646],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5757812,0.00001381635,0.4230414,0.00004502244,0.0002471861,0.0001095977,0.00002155808,0.0001847062,0.0005555402],"genre_scores_gemma":[0.9946312,0.00006246306,0.00404704,0.00007489182,0.0004986723,0.00003041496,0.0005480785,0.0000189253,0.00008833217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6893007,"threshold_uncertainty_score":0.543686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04048455179691896,"score_gpt":0.2800547044762974,"score_spread":0.2395701526793784,"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."}}