{"id":"W2894131099","doi":"10.1167/18.10.236","title":"Category-specific guidance of gaze in photographs and line drawings","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Salience (neuroscience); Line drawings; Orientation (vector space); Artificial intelligence; Grayscale; Computer vision; Luminance; Computer science; Psychology; Cognitive psychology; Mathematics; Image (mathematics); Geometry; Engineering drawing","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":[0.0004473722,0.0002722205,0.0002149494,0.0002613804,0.0001134204,0.0004525546,0.0002148771,0.000275552,0.00223007],"category_scores_gemma":[0.006706452,0.0002462293,0.0002934713,0.0001340296,0.0002096879,0.000560772,0.0004037701,0.0003234803,0.0002808842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003125509,"about_ca_system_score_gemma":0.0001806131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004532016,"about_ca_topic_score_gemma":0.005506054,"domain_scores_codex":[0.9997358,0.00005904332,0.000008912288,0.0001090259,0.00005213195,0.00003508137],"domain_scores_gemma":[0.9989086,0.0005825217,0.0002154334,0.0001415248,0.0001117151,0.00004020374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001155458,0.00006999114,0.08653493,0.0003021123,0.0001048298,0.0002654711,0.002253865,0.01093865,0.7706314,0.001407511,0.001395178,0.1249406],"study_design_scores_gemma":[0.0000313492,0.0004788826,0.8588663,0.00006218286,0.00006759626,0.000338362,0.0004938482,0.06990353,0.06651977,0.001971476,0.001220607,0.00004612866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986551,0.0001224554,0.01106117,0.00003423834,0.0000115036,0.00003269754,0.0001618021,0.0001770882,0.001847912],"genre_scores_gemma":[0.9949893,0.00005318575,0.004068681,0.00001117734,0.000001922213,0.0000177019,0.0001821731,0.00003287779,0.0006430011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004532016,"threshold_uncertainty_score":0.009011269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01632766351043103,"score_gpt":0.2991900743975507,"score_spread":0.2828624108871197,"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."}}