{"id":"W2568344295","doi":"10.1167/16.12.1291","title":"Psychophysical Evaluation of Saliency Algorithms","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Visual search; Computer science; Stimulus (psychology); Set (abstract data type); Artificial intelligence; Human visual system model; Range (aeronautics); Orientation (vector space); Psychophysics; Pattern recognition (psychology); Algorithm; Cognitive psychology; Perception; Psychology; Mathematics; Image (mathematics)","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":[0.001305788,0.00005326966,0.0001168927,0.0001481966,0.00003099563,0.00001962422,0.0002800249,0.00003330128,0.00003637198],"category_scores_gemma":[0.00009860201,0.00003017563,0.0001146867,0.0002321395,0.00002265421,0.0006653071,0.00003288381,0.00005490981,0.00002201589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004595566,"about_ca_system_score_gemma":0.00005505802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.984495e-7,"about_ca_topic_score_gemma":2.396461e-7,"domain_scores_codex":[0.998373,0.0001438936,0.0003762509,0.0000997664,0.0009277838,0.00007932073],"domain_scores_gemma":[0.9987156,0.00004142871,0.0003787328,0.0001613442,0.0006477102,0.00005519514],"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":[0.00001483503,0.0001663947,0.0001100887,0.000002554728,0.000007281793,0.000001069799,0.00008748408,0.00001622885,0.1817197,0.001570071,0.0005293164,0.8157749],"study_design_scores_gemma":[0.01104712,0.009656581,0.5290791,0.001177387,0.0001555278,0.0003812159,0.0001145818,0.1836735,0.1172138,0.1416923,0.005215736,0.0005931102],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4703718,0.00005574905,0.5268327,0.001113992,0.001152109,0.00005122584,3.294074e-7,0.00001044564,0.0004116542],"genre_scores_gemma":[0.9943447,0.00002779533,0.00543208,0.0000273775,0.0001249379,5.408575e-7,5.007494e-8,0.00000269653,0.00003981414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8151819,"threshold_uncertainty_score":0.1230526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03528221523696871,"score_gpt":0.3717158762221929,"score_spread":0.3364336609852241,"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."}}