{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009499534,0.000128844,0.0001497169,0.0001102701,0.00007115064,0.0002395534,0.0003805953,0.00006740594,0.0002549383],"category_scores_gemma":[0.00003008604,0.0001114396,0.00001921209,0.0001538636,0.00008553113,0.000629861,0.0002216863,0.0001493786,0.001564009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002877701,"about_ca_system_score_gemma":0.00001656382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000902097,"about_ca_topic_score_gemma":0.00005665498,"domain_scores_codex":[0.9989854,0.00004218779,0.0001364431,0.0004611247,0.0001295288,0.000245306],"domain_scores_gemma":[0.9993343,0.00006217599,0.00004408932,0.0003765616,0.00005299064,0.0001299032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004179486,0.0002442318,0.003524098,0.000007693241,0.00005926561,0.00005910425,0.0003591016,0.00009149854,0.3427918,0.003912999,0.3882869,0.2606215],"study_design_scores_gemma":[0.004316197,0.001130347,0.2330539,0.00004157309,0.00003750806,0.00005610017,0.0008986418,0.5313507,0.02926209,0.001712367,0.1968102,0.001330399],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5140992,0.000008825174,0.4835593,0.001544081,0.0001318753,0.000156523,0.0001277805,0.0002335602,0.0001389199],"genre_scores_gemma":[0.8943555,0.000006633314,0.1047243,0.0006123557,0.00005347356,0.00001703327,0.00006803654,0.000006856497,0.000155869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5312592,"threshold_uncertainty_score":0.9992134,"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."}}