{"id":"W2154298189","doi":"10.1109/ccece.2008.4564716","title":"Markerless human tracking for industrial environments","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Pixel; Tracking (education); Background subtraction; Robot","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003236574,0.000369166,0.0004085734,0.0003946575,0.0004272205,0.0004322589,0.0007682556,0.0002205753,0.00001044402],"category_scores_gemma":[0.00006535973,0.0003816173,0.00008225124,0.0003897066,0.0000643782,0.0004676485,0.00008488003,0.0004777865,0.000006499567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001457543,"about_ca_system_score_gemma":0.0002989772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005047797,"about_ca_topic_score_gemma":0.0001396428,"domain_scores_codex":[0.9977165,0.00001858728,0.0003407655,0.0007685781,0.0002721518,0.0008834417],"domain_scores_gemma":[0.998838,0.000112778,0.00009385835,0.0002003522,0.0001332781,0.0006217372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000384675,0.0001222463,0.01966772,0.00008167639,0.0001256583,0.00009590735,0.001378291,0.0001915817,0.003927488,0.5815639,0.001478391,0.3913286],"study_design_scores_gemma":[0.001596296,0.0007381312,0.04322686,0.0001995567,0.00001806158,0.0001901451,0.00001351574,0.936233,0.001761972,0.003578115,0.01117156,0.001272806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.155227,0.00007083193,0.841434,0.0008592777,0.0004335344,0.0005868573,0.000009502934,0.0002584677,0.00112053],"genre_scores_gemma":[0.9870399,0.00004867635,0.0121301,0.000277007,0.0002961863,0.00007210497,0.000005835449,0.00002551975,0.00010463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9360414,"threshold_uncertainty_score":0.9998636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06542515969268059,"score_gpt":0.2417770365986049,"score_spread":0.1763518769059244,"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."}}