{"id":"W2014865990","doi":"10.3389/fnins.2015.00139","title":"Toward a unified view of the speed-accuracy trade-off","year":2015,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science","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":[0.0003954338,0.0001748709,0.0002528282,0.000131134,0.00008614106,0.00008138242,0.001817627,0.0000491001,0.000003245881],"category_scores_gemma":[0.001749417,0.0001214727,0.00008322932,0.001299414,0.000712436,0.0003992719,0.000307883,0.0002735642,0.00000394028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000046732,"about_ca_system_score_gemma":0.0001568081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001144287,"about_ca_topic_score_gemma":0.000001794315,"domain_scores_codex":[0.997772,0.0002870001,0.0003570388,0.0005837425,0.0005999969,0.0004001552],"domain_scores_gemma":[0.9990186,0.0001314354,0.0001790237,0.0005184262,0.00002098139,0.0001315166],"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":[0.00015799,0.0006500639,0.02628796,0.0001378609,0.000002837969,0.0001599413,0.00846272,0.003976625,0.8208833,0.004366314,0.07314808,0.06176633],"study_design_scores_gemma":[0.001272245,0.0004111848,0.01661888,0.0001948214,0.00001070594,0.0001618257,0.0005988737,0.04477927,0.7350966,0.004264645,0.1960229,0.0005681024],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970748,0.000484041,0.003952136,0.005072682,0.01266747,0.0006494281,0.00002108475,0.0001028147,0.006302333],"genre_scores_gemma":[0.9960536,0.00008275235,0.0006286941,0.00285251,0.00003883062,0.000004107863,1.033673e-7,0.00001209938,0.0003272716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1228748,"threshold_uncertainty_score":0.4953513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07113556672496517,"score_gpt":0.2883044284063732,"score_spread":0.217168861681408,"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."}}