{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003680429,0.0002755622,0.0002224093,0.0003993105,0.0001309342,0.0002870885,0.0001091143,0.00016303,0.001197747],"category_scores_gemma":[0.001965765,0.0001444303,0.0002089299,0.0001350283,0.0002513739,0.0002313542,0.0003432951,0.0003158028,0.000126852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002263625,"about_ca_system_score_gemma":0.0002529351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002785419,"about_ca_topic_score_gemma":0.002971903,"domain_scores_codex":[0.9998019,0.00002692917,0.00001720438,0.00007053253,0.00003775737,0.00004567536],"domain_scores_gemma":[0.9989271,0.0003330337,0.0003107993,0.00007436163,0.0001629464,0.0001918667],"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":[0.002789077,0.00028249,0.1654034,0.0001761464,0.00005985836,0.001069231,0.001466064,0.0002573761,0.7819686,0.0002674886,0.000518129,0.04574217],"study_design_scores_gemma":[0.00001059368,0.0007681509,0.9528642,0.00001124218,0.00003222881,0.0003291247,0.0002434598,0.0004160467,0.04470156,0.00007894866,0.0005343924,0.00001005241],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989373,0.0001648436,0.0003455556,0.00001646015,0.000004049996,0.000007471358,0.0001724426,0.00001624696,0.0003355975],"genre_scores_gemma":[0.9982633,0.000154851,0.0007915489,0.00001785971,0.000003179916,0.00003451333,0.0002136387,0.00001496693,0.0005061188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002785419,"threshold_uncertainty_score":0.005538464,"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."}}