{"id":"W2151867154","doi":"10.1037//0882-7974.15.2.253","title":"How aging affects the reading of words in noisy backgrounds.","year":2000,"lang":"en","type":"article","venue":"Psychology and Aging","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychology; Reading (process); Context (archaeology); Word (group theory); Noise (video); Audiology; Cognitive psychology; Developmental psychology; Linguistics; Artificial intelligence","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":[0.000294249,0.00009915616,0.0001457398,0.00008020766,0.0001101792,0.00002545535,0.000186474,0.00006024401,0.00004925064],"category_scores_gemma":[0.00007223844,0.00006919424,0.00002963792,0.0002083646,0.0002768166,0.00009987833,0.00002937682,0.0002517445,0.000005871804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003259292,"about_ca_system_score_gemma":0.000004689607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001131733,"about_ca_topic_score_gemma":0.00001273433,"domain_scores_codex":[0.9990487,0.0002118688,0.0001103787,0.0003272456,0.00005150049,0.0002503039],"domain_scores_gemma":[0.9993794,0.0003427491,0.00005054791,0.0001989224,0.000003904403,0.00002450669],"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":[0.00004169469,0.0000601797,0.009016127,0.00002454026,0.000005104621,0.0005673814,0.004336075,0.000006380543,0.851931,0.0003879701,0.0001830486,0.1334405],"study_design_scores_gemma":[0.008824862,0.0009286108,0.1417292,0.001012487,0.000118151,0.01077471,0.004402787,0.0007897674,0.7411902,0.01537801,0.07270598,0.002145238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874848,0.0004710463,0.00001556436,0.005838972,0.0001836258,0.00008411311,0.000001140592,0.00002587073,0.005894863],"genre_scores_gemma":[0.9944102,0.0002906946,0.00004739204,0.004349648,0.00007805993,0.000004023774,5.078922e-7,0.000007650448,0.0008118831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1327131,"threshold_uncertainty_score":0.2821659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848701911090451,"score_gpt":0.3190253465101647,"score_spread":0.2905383273992602,"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."}}