{"id":"W4254006548","doi":"10.1155/2005/693254","title":"Visual Warning Signals Optimized for Human Perception: What the Eye Sees Fastest","year":2005,"lang":"en","type":"article","venue":"Applied Bionics and Biomechanics","topic":"Ocular and Laser Science Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Federal Highway Administration; U.S. Department of Transportation; National Institutes of Health; California Department of Transportation; National Research Council Canada; Federal Transit Administration","keywords":"Computer science; SIGNAL (programming language); Computer vision; Human visual system model; Contrast (vision); Detection threshold; Artificial intelligence; Visual perception; Sensitivity (control systems); Energy (signal processing); Perception; Psychophysics; Sine; Real-time computing; Mathematics; Engineering; Electronic engineering; Psychology; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000515953,0.0002934011,0.0002723228,0.0002166647,0.00010025,0.0005984837,0.0001794835,0.0003993212,0.001370958],"category_scores_gemma":[0.004795151,0.0001455958,0.0001934993,0.0001358827,0.0002217111,0.001179726,0.0001791535,0.0002132164,0.0002205702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002691392,"about_ca_system_score_gemma":0.0001624085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002600096,"about_ca_topic_score_gemma":0.0002949237,"domain_scores_codex":[0.9998258,0.00005081144,0.000009675446,0.00005450492,0.00003661693,0.00002273497],"domain_scores_gemma":[0.9990583,0.0004670367,0.0002035625,0.00006023491,0.0001420693,0.00006875049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002621354,0.0001204689,0.01241265,0.0007556546,0.000111403,0.0001941369,0.000417997,0.02437651,0.8024258,0.006722309,0.001107032,0.1487346],"study_design_scores_gemma":[0.0006440679,0.006490879,0.1863978,0.0002553583,0.0005620147,0.001291555,0.000836074,0.3204979,0.423089,0.0502759,0.009365867,0.0002935781],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8971344,0.001225384,0.09744511,0.0004197694,0.00008203176,0.00005189778,0.00009039291,0.0002994114,0.003251648],"genre_scores_gemma":[0.9786814,0.0004026241,0.02011743,0.00008026034,0.00003925737,0.00002083979,0.00004197102,0.00006081755,0.0005554246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001370958,"threshold_uncertainty_score":0.004586339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437569414521093,"score_gpt":0.3390943076519143,"score_spread":0.3147186135067034,"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."}}