{"id":"W2204369384","doi":"10.1016/j.visres.2015.09.007","title":"Computational models of visual attention","year":2015,"lang":"en","type":"editorial","venue":"Vision Research","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Eye Institute","keywords":"Visual attention; Psychology; Optometry; Computer science; Neuroscience; Perception; Medicine","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":[0.003863956,0.001994828,0.001746941,0.002911948,0.001037284,0.004214149,0.003579996,0.009943753,0.005351993],"category_scores_gemma":[0.0136357,0.001036013,0.001486803,0.0009537107,0.004256804,0.00489773,0.001402848,0.01639442,0.00307468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003229835,"about_ca_system_score_gemma":0.00229267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002626025,"about_ca_topic_score_gemma":0.004685608,"domain_scores_codex":[0.9986548,0.0003464094,0.000138489,0.0002080123,0.0005678959,0.00008440585],"domain_scores_gemma":[0.9899161,0.00685017,0.0003306773,0.0003159328,0.002081892,0.0005051774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003695389,0.00001165291,0.00003381885,0.0003332756,0.0000575497,0.00009459853,0.00001637636,0.0003708919,0.00009642651,0.02159387,0.9524326,0.02492202],"study_design_scores_gemma":[0.00008692618,0.00002278678,0.0002118769,0.0004602003,0.0000953038,0.0002663096,0.00001770996,0.002253989,0.0002464549,0.0730134,0.9232848,0.00004026755],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001789794,0.08081925,0.005732201,0.1461105,0.7612743,0.00001628491,0.0001720316,0.0002228697,0.005473498],"genre_scores_gemma":[0.005084762,0.04760407,0.001531037,0.02266622,0.9082491,0.00005283994,0.0001430515,0.0000778127,0.01459097],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.009943753,"threshold_uncertainty_score":0.02343422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08810110300873669,"score_gpt":0.4617985535996165,"score_spread":0.3736974505908798,"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."}}