{"id":"W2092182902","doi":"10.1167/3.9.330","title":"Multiple object tracking is scene-based, not image-based","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Motion (physics); Object (grammar); Observer (physics); Match moving; Affine transformation; Tracking (education); Translation (biology); Representation (politics); Structure from motion; Mathematics; Physics; Psychology; Geometry","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":[0.0007097709,0.0002465488,0.000324173,0.0002193452,0.0001747878,0.0007553577,0.0002993671,0.0003483234,0.001382346],"category_scores_gemma":[0.004006649,0.0002399132,0.0002276305,0.0002400153,0.0005151383,0.001488576,0.000558216,0.0004581954,0.0002699796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002911682,"about_ca_system_score_gemma":0.0003492101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001767223,"about_ca_topic_score_gemma":0.001978867,"domain_scores_codex":[0.9996052,0.0000505021,0.00003234073,0.0001041404,0.0001587787,0.00004909581],"domain_scores_gemma":[0.9980447,0.0005372191,0.000646587,0.0004079486,0.0002396275,0.0001238042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001183089,0.0001193049,0.03280949,0.0003537529,0.00009072442,0.0001570807,0.0004548875,0.002253037,0.8382713,0.002332764,0.0004404179,0.1215341],"study_design_scores_gemma":[0.0001052316,0.001954972,0.6064136,0.0001072873,0.0001783967,0.0009355671,0.0002487706,0.03264577,0.3489983,0.003325026,0.005008495,0.00007863733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733976,0.0004171288,0.02017306,0.00008916957,0.0000351145,0.00004326892,0.0001026684,0.0002089755,0.005533073],"genre_scores_gemma":[0.9930865,0.0001994307,0.005463532,0.00004625204,0.000008491381,0.00001415088,0.0001687365,0.0000328089,0.0009799863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001767223,"threshold_uncertainty_score":0.004624426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192999827381786,"score_gpt":0.2980198760513167,"score_spread":0.2860898777774988,"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."}}