{"id":"W1991377483","doi":"10.1167/11.11.469","title":"Effects of development on low-level feature processing during natural viewing of dynamic scenes","year":2011,"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":"Queen's University","funders":"","keywords":"Gaze; Salience (neuroscience); Contrast (vision); Correlation; Saccade; Psychology; Eye tracking; Artificial intelligence; Discriminative model; Feature (linguistics); Eye movement; Computer vision; Audiology; Computer science; Pattern recognition (psychology); Communication; Mathematics; 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":[0.0002836521,0.00008917359,0.0001913403,0.0002448969,0.00007741425,0.00001998157,0.0002712748,0.00004644463,0.000001627587],"category_scores_gemma":[0.00004345497,0.00006411442,0.0000895233,0.0002450089,0.00001615753,0.000438989,0.00006137469,0.0001733575,0.000001523395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005452424,"about_ca_system_score_gemma":0.00005858458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.971551e-7,"about_ca_topic_score_gemma":5.184975e-7,"domain_scores_codex":[0.9989724,0.0000488165,0.0003685977,0.0001094917,0.0003934473,0.0001072655],"domain_scores_gemma":[0.9990913,0.00002300369,0.0005381575,0.00008409935,0.0002205899,0.00004281797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001231749,0.0003216444,0.0003229823,0.0006831916,0.00002390367,0.00002532517,0.003731341,0.00002255385,0.6734408,0.00003721568,0.000009435646,0.3212585],"study_design_scores_gemma":[0.0005463989,0.0003667773,0.5476186,0.002177314,0.00000835658,0.00005521835,0.00003790748,0.003707489,0.4453076,0.00007786545,0.00001031511,0.00008616536],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973014,0.0003743542,0.02598636,0.00003051105,0.0005018376,0.00005599458,7.47099e-8,0.000009974147,0.00002686501],"genre_scores_gemma":[0.9799429,0.00002020454,0.01995874,0.00001642703,0.00001760292,3.958071e-7,9.532182e-8,0.00000483799,0.00003881983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5472956,"threshold_uncertainty_score":0.261451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01642787723655224,"score_gpt":0.2791489957521772,"score_spread":0.262721118515625,"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."}}