{"id":"W2202819085","doi":"10.1371/journal.pone.0138053","title":"Visual Saliency Prediction and Evaluation across Different Perceptual Tasks","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Computer science; Fixation (population genetics); Benchmarking; Artificial intelligence; Eye tracking; Perception; Visual perception; Gaze; Eye movement; Visual search; Pattern recognition (psychology); Psychophysics; Computational model; Machine learning; Computer vision; Population","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005389356,0.0001088341,0.0001269265,0.000061698,0.0001483801,0.0001556166,0.0001555027,0.00006656151,0.00002346786],"category_scores_gemma":[0.00009916583,0.00009761002,0.00002632067,0.0001829727,0.00004384993,0.0005364994,0.0001333526,0.0001050049,0.00006550299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009648343,"about_ca_system_score_gemma":0.00003101102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001469383,"about_ca_topic_score_gemma":0.00001458551,"domain_scores_codex":[0.9982588,0.0001414996,0.0001988288,0.0003357168,0.0008640506,0.0002010598],"domain_scores_gemma":[0.9993231,0.00001655896,0.00006258659,0.0001967709,0.0002438866,0.0001570543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002259606,0.01668055,0.1694467,0.0002587157,0.0004806372,0.00001114912,0.06471476,0.0002010022,0.290729,0.008330096,0.001745394,0.447176],"study_design_scores_gemma":[0.001619677,0.001329633,0.2400944,0.00006426457,0.00006977459,0.00001136356,0.0009118683,0.743011,0.01076573,0.001794571,0.00005800219,0.0002697094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719077,0.00005949784,0.02668322,0.0002370718,0.0002523133,0.0002613282,0.000003973038,0.0002038234,0.0003911012],"genre_scores_gemma":[0.998767,0.0000141711,0.0007537599,0.00007981173,0.0001200697,0.00004661608,0.00001299325,0.000006733459,0.000198803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.74281,"threshold_uncertainty_score":0.3980421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1213671869892572,"score_gpt":0.3276533720032668,"score_spread":0.2062861850140096,"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."}}