{"id":"W1462800562","doi":"10.1167/15.12.1361","title":"When is stereopsis useful in visual search?","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Stereopsis; Computer science; Psychology; Optometry; Artificial intelligence; Computer vision; 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":[0.001019419,0.00006519096,0.000133596,0.0003342096,0.00002886528,0.0001231519,0.0003417395,0.00004557652,0.0000279176],"category_scores_gemma":[0.00004719527,0.00005064926,0.00007366302,0.0003075301,0.00001329898,0.0009244904,0.0001052514,0.0001854753,0.0000671779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008607875,"about_ca_system_score_gemma":0.00007922437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001435429,"about_ca_topic_score_gemma":0.000005464273,"domain_scores_codex":[0.9986907,0.0001237743,0.0003518627,0.0001159806,0.0005834027,0.0001342627],"domain_scores_gemma":[0.9993148,0.00002302257,0.0001441804,0.0001241835,0.0002428132,0.0001509901],"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":[0.0005635897,0.002397334,0.08260876,0.0000660396,0.00007831419,0.0005689948,0.04468518,0.000625446,0.02805392,0.001636519,0.06795657,0.7707593],"study_design_scores_gemma":[0.009627993,0.01344147,0.5888305,0.0007208243,0.00002827037,0.001220528,0.002582838,0.2790199,0.02174383,0.03096084,0.05092655,0.0008964985],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241245,0.00006309442,0.0728636,0.001982049,0.0006039907,0.00004178668,1.77164e-7,0.00001350651,0.0003073476],"genre_scores_gemma":[0.9956812,0.00001239428,0.003683839,0.000339334,0.00008266693,2.721096e-7,1.008769e-7,0.000004013367,0.0001961834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7698628,"threshold_uncertainty_score":0.2065417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05261897651395098,"score_gpt":0.3553309539070773,"score_spread":0.3027119773931263,"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."}}