{"id":"W2007391188","doi":"10.1167/12.9.740","title":"On-Line Contributions of Peripheral Information to Visual Search in Scenes: Further Explorations of Object Content and Scene Context","year":2012,"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":"Queen's University","funders":"","keywords":"Computer vision; Artificial intelligence; Scene statistics; Object (grammar); Computer science; Visual search; Context (archaeology); Gaze; Representation (politics); Perception; Geography; Psychology","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":[0.000904789,0.00007051249,0.0001976405,0.0003981796,0.00004746148,0.00003534405,0.0001233831,0.00004325623,0.00001300672],"category_scores_gemma":[0.0001827397,0.000055175,0.00006283,0.0003717937,0.00003043956,0.001445579,0.00004910816,0.0001199186,0.000005570811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006906454,"about_ca_system_score_gemma":0.00005508005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002548051,"about_ca_topic_score_gemma":0.00001024353,"domain_scores_codex":[0.9987103,0.0001172805,0.0006116892,0.00005765204,0.0003680196,0.0001350026],"domain_scores_gemma":[0.9988685,0.00006804337,0.0002732637,0.00009203385,0.0005881988,0.0001100101],"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":[0.001707092,0.004016707,0.02531582,0.0001735596,0.0001226969,0.000005733903,0.04497576,0.008814597,0.4045559,0.04139588,0.0005880761,0.4683282],"study_design_scores_gemma":[0.009811059,0.0168798,0.6326694,0.001490409,0.00004030346,0.0001587742,0.01207554,0.1570575,0.1675834,0.0008586936,0.0008571235,0.0005179562],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7700505,0.00007783558,0.2288272,0.0006599236,0.0002353151,0.0001178046,0.000003468032,0.000004510893,0.00002345231],"genre_scores_gemma":[0.9986534,0.00003166213,0.001132211,0.0001336433,0.00003507319,0.000001950624,0.000001314464,0.000002488316,0.000008249094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6073536,"threshold_uncertainty_score":0.2249971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0483195952565796,"score_gpt":0.3579606717785155,"score_spread":0.3096410765219359,"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."}}