{"id":"W2120697543","doi":"","title":"Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Leverage (statistics); Eye movement; Eye tracking; Machine learning; Hidden Markov model; Visual search; Task (project management); Pattern recognition (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006895721,0.001586058,0.0009024892,0.00164076,0.0003233628,0.0007086712,0.001734173,0.001634821,0.002141184],"category_scores_gemma":[0.003428082,0.0004184944,0.001486687,0.001095765,0.0005124989,0.0008491655,0.0007945225,0.001373952,0.001100942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009410613,"about_ca_system_score_gemma":0.001105742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02251009,"about_ca_topic_score_gemma":0.03356327,"domain_scores_codex":[0.999478,0.0000660905,0.00003345288,0.0002716536,0.00008641152,0.00006432862],"domain_scores_gemma":[0.9989698,0.0003508863,0.0001374359,0.0003312156,0.0001457376,0.00006492402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001282257,0.001861079,0.02033678,0.001481463,0.0007095026,0.0005284525,0.0001654717,0.3765482,0.03121396,0.004217346,0.06496933,0.4966861],"study_design_scores_gemma":[0.0000721526,0.0002178406,0.01000084,0.00004465477,0.00004786004,0.0001390109,0.00004165522,0.9759102,0.005841483,0.003006757,0.004638535,0.00003898612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5631217,0.009779401,0.2897983,0.001575454,0.0005564424,0.0008662648,0.09756392,0.03030854,0.006430054],"genre_scores_gemma":[0.6918572,0.0008801536,0.1550736,0.0002711015,0.0001906455,0.0005395786,0.1468222,0.0004556343,0.003909972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02251009,"threshold_uncertainty_score":0.04475814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657274910110922,"score_gpt":0.2612844572372328,"score_spread":0.2447117081361236,"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."}}