{"id":"W4412439384","doi":"10.1167/jov.25.9.2428","title":"The contribution of motion detectors during multiple-object tracking","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Tracking (education); Computer vision; Detector; Motion (physics); Artificial intelligence; Object (grammar); Computer science; Physics; Optics; 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":[],"consensus_categories":[],"category_scores_codex":[0.002161364,0.00006690643,0.0001692684,0.0001525173,0.0001962299,0.0000900485,0.000390538,0.00004558541,5.982954e-7],"category_scores_gemma":[0.001072528,0.00004270572,0.0001358252,0.000365132,0.00002604585,0.0003966149,0.00006372866,0.0001796416,6.069075e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005634797,"about_ca_system_score_gemma":0.00005047558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004591192,"about_ca_topic_score_gemma":0.00001031561,"domain_scores_codex":[0.9988041,0.0002444596,0.0004598382,0.00009168467,0.000267128,0.0001328009],"domain_scores_gemma":[0.9982574,0.0006818848,0.0004476807,0.0001992343,0.0003869918,0.00002679962],"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":[0.0001904163,0.00009993051,0.08470368,0.0000361298,0.00007908908,0.0000195743,0.0002949958,0.00191745,0.220665,0.001990321,0.00005605358,0.6899474],"study_design_scores_gemma":[0.0007788165,0.0001259202,0.8332013,0.0002441372,0.000008953742,0.00002878113,0.00002077789,0.007607644,0.155643,0.002040539,0.0002489066,0.00005122989],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5515396,0.0004510065,0.4469872,0.000290319,0.0006502979,0.00004048391,1.384425e-7,0.0000100789,0.00003088693],"genre_scores_gemma":[0.9950173,0.00009593357,0.004802272,0.00001405025,0.00005794315,3.94753e-7,9.347421e-8,0.000002539803,0.00000940152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7484976,"threshold_uncertainty_score":0.1741489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01199363457523242,"score_gpt":0.3099491748070818,"score_spread":0.2979555402318494,"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."}}