{"id":"W2111521672","doi":"10.1109/robot.2006.1642259","title":"Kinematic approach for the evaluation of human visual perceptibility in the workspace","year":2006,"lang":"en","type":"article","venue":"","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Workspace; Computer vision; Kinematics; Artificial intelligence; Computer science; Observer (physics); Human visual system model; Focus (optics); Motion (physics); Image (mathematics); Optics; Physics","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":[0.00120059,0.001161422,0.0007002051,0.003430119,0.0003749262,0.001044813,0.0005710438,0.000666164,0.003965452],"category_scores_gemma":[0.005162458,0.0003841097,0.0006264747,0.001212638,0.0005712443,0.001218912,0.0009117937,0.0004978165,0.0008394319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003991309,"about_ca_system_score_gemma":0.0005340776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001418925,"about_ca_topic_score_gemma":0.001375425,"domain_scores_codex":[0.9987231,0.0003945417,0.0001082545,0.0002009721,0.0005178722,0.00005521503],"domain_scores_gemma":[0.9975332,0.0009961711,0.0003678266,0.0001867704,0.0008020553,0.0001138893],"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":[0.001316264,0.0003621667,0.02355387,0.001731038,0.0005224454,0.0004339129,0.001412394,0.02748818,0.2810552,0.01140022,0.002156353,0.648568],"study_design_scores_gemma":[0.0004011921,0.005995956,0.2326469,0.0008521235,0.0007667302,0.006891188,0.002590302,0.5383036,0.1519448,0.02616417,0.03243124,0.001011835],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02651808,0.0006839011,0.969219,0.00003944521,0.00006429587,0.0002215403,0.0002350652,0.0004180799,0.002600739],"genre_scores_gemma":[0.4012605,0.001293778,0.5932325,0.00006410439,0.0001266137,0.001027631,0.0005104912,0.0001517295,0.002332689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003965452,"threshold_uncertainty_score":0.01326573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06457726743463332,"score_gpt":0.4071883602776087,"score_spread":0.3426110928429754,"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."}}