{"id":"W2039934771","doi":"10.1016/j.visres.2011.07.002","title":"The where, what and when of gaze allocation in the lab and the natural environment","year":2011,"lang":"en","type":"article","venue":"Vision Research","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":544,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; Eye movement; Psychology; Natural (archaeology); Perspective (graphical); Eye tracking; Motion (physics); Cognitive psychology; Computer science; Computer vision; Artificial intelligence; Geography","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":[0.003766296,0.00004154576,0.00004800734,0.00005928694,0.000273278,0.0002374942,0.00044251,0.00002381262,0.000009996337],"category_scores_gemma":[0.00004528682,0.00001727406,0.00001534407,0.0001802427,0.0003220051,0.0003245726,0.0002572004,0.0002007486,0.000009947574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001071568,"about_ca_system_score_gemma":0.00001021859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001737948,"about_ca_topic_score_gemma":0.00009281546,"domain_scores_codex":[0.998421,0.0006931585,0.0001247097,0.0001534175,0.0004748523,0.0001328636],"domain_scores_gemma":[0.999289,0.0003135768,0.00002968476,0.000305049,0.00004295662,0.00001967213],"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":[0.0001769987,0.0001366518,0.0008993202,0.00002445073,0.00001345522,0.000002809092,0.0278014,0.000002603526,0.00322853,0.1459049,0.001299726,0.8205091],"study_design_scores_gemma":[0.004327699,0.001563598,0.5505611,0.0002845048,0.00001199761,0.00005239757,0.02754756,0.2325194,0.005883677,0.1339793,0.04291605,0.0003528013],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9422638,0.01128608,0.006855001,0.03654332,0.0003187005,0.001128776,3.841918e-7,0.00002168936,0.001582298],"genre_scores_gemma":[0.9957461,0.003605249,0.0001913778,0.00007030832,0.000008734325,0.00002122964,1.526569e-7,0.000001858008,0.0003549447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8201563,"threshold_uncertainty_score":0.2290162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05813764863864399,"score_gpt":0.3455197120911833,"score_spread":0.2873820634525393,"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."}}