{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003078572,0.0004100005,0.0004327319,0.00102303,0.0002466015,0.0006698007,0.0002674573,0.0005412305,0.002009953],"category_scores_gemma":[0.002183732,0.0002509232,0.0002139459,0.0007687125,0.0002585748,0.0007089532,0.0007304229,0.0004901374,0.0008335174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001305707,"about_ca_system_score_gemma":0.000260643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009027102,"about_ca_topic_score_gemma":0.001645359,"domain_scores_codex":[0.9998191,0.0000377062,0.000008093441,0.00004251196,0.00005281045,0.00003974555],"domain_scores_gemma":[0.9997206,0.00009795019,0.00004428793,0.00003099308,0.00007670212,0.00002933779],"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":[0.0009307913,0.0001316803,0.005960846,0.0003911572,0.00007609666,0.0003615236,0.0002324354,0.01326475,0.353024,0.004666666,0.003590807,0.6173691],"study_design_scores_gemma":[0.0001432592,0.001064276,0.1584952,0.0004086317,0.0003147853,0.003405927,0.0007375269,0.5860079,0.1948043,0.03200972,0.02240541,0.0002030498],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3466211,0.006045786,0.6347803,0.0004786041,0.0004387726,0.0001183099,0.001364768,0.001581495,0.00857091],"genre_scores_gemma":[0.8836591,0.002057337,0.1100456,0.0001293347,0.0001457311,0.00005862576,0.0008194123,0.0001179261,0.002967017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002009953,"threshold_uncertainty_score":0.006724,"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."}}