{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004583634,0.0001982733,0.0003419075,0.001068398,0.0002196977,0.0002720633,0.001206378,0.000580234,0.00003364149],"category_scores_gemma":[0.0004418227,0.0001836325,0.0001610927,0.001337516,0.0001590832,0.0006803999,0.0007973633,0.00109495,0.0002919865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002110376,"about_ca_system_score_gemma":0.0008970547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001055754,"about_ca_topic_score_gemma":0.000006805548,"domain_scores_codex":[0.9916911,0.0007466181,0.0005795924,0.0007243402,0.005836943,0.0004213888],"domain_scores_gemma":[0.993786,0.0006899872,0.0002134339,0.0005124277,0.004595257,0.0002028469],"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":[0.00003058403,0.0002536181,0.000001707143,0.00007721628,0.00002064506,0.000005374269,0.00008607234,0.001612446,0.000156648,0.003861111,0.9834186,0.01047602],"study_design_scores_gemma":[0.0007394429,0.001360943,0.00004551299,0.0002063837,0.000007204922,0.00000216852,0.00004574325,0.7262446,0.0000565834,0.04347475,0.2275506,0.0002660955],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0008211582,0.0001986269,0.3883725,0.0002606499,0.606166,0.0004549455,0.0000395836,0.0001737276,0.003512789],"genre_scores_gemma":[0.1659618,0.0003604733,0.02107346,0.00004306151,0.792489,0.0001848988,0.001393266,0.0001745975,0.01831941],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.755868,"threshold_uncertainty_score":0.7488315,"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."}}