{"id":"W2103666701","doi":"10.1167/9.3.5","title":"Saliency, attention, and visual search: An information theoretic approach","year":2009,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":855,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Fixation (population genetics); Computation; Premise; Visual cortex; Coding (social sciences); Artificial intelligence; Variety (cybernetics); Computational model; Theoretical computer science; Pattern recognition (psychology); Machine learning; Neuroscience; Psychology; Algorithm; Mathematics; Biology","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":[0.0009495098,0.00007767492,0.0001137239,0.0002913511,0.0001314781,0.0002807952,0.0002279032,0.00004983079,0.000005571566],"category_scores_gemma":[0.00003106688,0.00005829363,0.0000584632,0.0002904775,0.00003090209,0.003545803,0.00003704609,0.0001550928,0.000009509534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001930151,"about_ca_system_score_gemma":0.0000286309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001330797,"about_ca_topic_score_gemma":1.147624e-7,"domain_scores_codex":[0.9988082,0.0001233336,0.0003812104,0.00009837688,0.0004639988,0.0001248754],"domain_scores_gemma":[0.9992967,0.00001313705,0.0002102158,0.0001057018,0.0002495745,0.0001247079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009135559,0.0006065461,0.0007008077,0.00002975754,0.00001132212,0.000007093437,0.002330884,0.0002191883,0.01635062,0.07189962,0.0003201752,0.9074326],"study_design_scores_gemma":[0.001563769,0.007647055,0.5592433,0.00009501691,0.00001989974,0.000916332,0.0008107053,0.4156634,0.001081257,0.01175467,0.0009090523,0.0002955457],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4877191,0.00002817202,0.510968,0.0004016958,0.0001546765,0.00004923148,1.399225e-7,0.00002309841,0.0006558507],"genre_scores_gemma":[0.9912237,0.00004647739,0.008405324,0.0002447435,0.00006083255,2.501792e-7,0.000001470929,0.000001981203,0.00001521946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9071371,"threshold_uncertainty_score":0.2707714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546226723078853,"score_gpt":0.3087286646497342,"score_spread":0.2932663974189457,"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."}}