{"id":"W7027961893","doi":"","title":"Dynamics of spatial attention during motion tracking: Characterization and modeling as a function of motion predictability","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Predictability; Motion (physics); Dynamics (music); Tracking (education); Task (project management); Motion perception; Eye movement; Process (computing); Eye tracking","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":[0.0006794614,0.000265713,0.0002864895,0.0003670231,0.0001620044,0.0004470963,0.0003738503,0.0004107998,0.0004154162],"category_scores_gemma":[0.002912713,0.0002991158,0.0005091184,0.0002219399,0.0002772839,0.0003922455,0.000341819,0.0004678923,0.00007088097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006179717,"about_ca_system_score_gemma":0.0005345785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01581882,"about_ca_topic_score_gemma":0.009099932,"domain_scores_codex":[0.9998614,0.00002718872,0.000008779712,0.00005231805,0.0000205994,0.00002971079],"domain_scores_gemma":[0.999113,0.000522613,0.0001920309,0.00006213129,0.00007562499,0.00003454643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003959984,0.0002326496,0.1099635,0.00009732728,0.0001545907,0.0002045457,0.000482393,0.813335,0.03725912,0.0026641,0.0003564115,0.03485432],"study_design_scores_gemma":[0.000002063269,0.00001750584,0.01778377,0.000002123343,0.000005582378,0.00001533753,0.000007883992,0.9815322,0.0004219317,0.0001788404,0.00002893351,0.000003808572],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731361,0.0001060492,0.02618206,0.0000805362,0.000005572743,0.00001669205,0.00005232709,0.0000415531,0.0003792839],"genre_scores_gemma":[0.9979407,0.00004726979,0.001660923,0.000007959686,0.000002954522,0.00001396116,0.00004828616,0.000004935038,0.0002730648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01581882,"threshold_uncertainty_score":0.03145349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05931325205512331,"score_gpt":0.2985719001107125,"score_spread":0.2392586480555892,"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."}}