{"id":"W2087934234","doi":"10.1037/a0015385","title":"Modeling performance at the trial level within a diffusion framework: A simple yet powerful method for increasing efficiency via error detection and correction.","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Computer science; Task (project management); Context (archaeology); Process (computing); Binary number; Error detection and correction; Diffusion; Simple (philosophy); Artificial intelligence; Machine learning; Algorithm; Arithmetic; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004334746,0.001069467,0.001235523,0.0009446097,0.0004765967,0.001654087,0.002852997,0.002046243,0.002971972],"category_scores_gemma":[0.02051834,0.000777163,0.001531039,0.0008739184,0.001332747,0.003182924,0.001828163,0.00234934,0.0007147013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158754,"about_ca_system_score_gemma":0.001269167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006637553,"about_ca_topic_score_gemma":0.004915898,"domain_scores_codex":[0.9986298,0.0005622525,0.00009158882,0.0003546179,0.0002582319,0.0001035914],"domain_scores_gemma":[0.9931011,0.004468145,0.0008415258,0.001027376,0.000336527,0.0002253124],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004175656,0.0002881977,0.00584142,0.0001896458,0.0003515246,0.0001797507,0.0005491465,0.7921991,0.01683734,0.09539188,0.001013251,0.08674109],"study_design_scores_gemma":[0.00001498595,0.00005464063,0.0004279875,0.000006907852,0.000023112,0.00003867402,0.00000697356,0.9792012,0.0009351888,0.01878253,0.0004875982,0.00002027734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02084593,0.0001583042,0.9773118,0.0001902467,0.00003607793,0.00006787378,0.00005466163,0.0003223669,0.001012855],"genre_scores_gemma":[0.6299335,0.0004057855,0.3625881,0.0001241121,0.00006567509,0.0004204922,0.0001290507,0.0002340964,0.006099273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9956653,"threshold_uncertainty_score":0.0229246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07066695442567318,"score_gpt":0.3425809967205009,"score_spread":0.2719140422948277,"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."}}