{"id":"W2149363368","doi":"10.1109/icsmc.2009.5346580","title":"An informatic rationale for the speed-accuracy trade-off","year":2009,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Imperfect; Computer science; Noise (video); Channel (broadcasting); Algorithm; Artificial intelligence; Telecommunications","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":[0.000107182,0.00007212422,0.00006172754,0.00002406548,0.0001695456,0.0001650854,0.0003387175,0.00001895897,0.00008429516],"category_scores_gemma":[0.0001492212,0.00004084392,0.00004135878,0.0000791827,0.00003097032,0.0005513512,0.000009833835,0.00005403241,0.0000364433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006334422,"about_ca_system_score_gemma":0.0000180347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001157034,"about_ca_topic_score_gemma":0.000002034573,"domain_scores_codex":[0.9994385,0.00001945139,0.0001555481,0.0001162647,0.000128679,0.0001415938],"domain_scores_gemma":[0.9990625,0.0006622935,0.00004319294,0.00018536,0.0000100462,0.0000365563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008181731,0.0003561277,0.00003716055,0.00003089615,0.000008912678,0.000003026884,0.007765774,0.004053634,0.3552684,0.1105871,0.07863867,0.4431684],"study_design_scores_gemma":[0.0005309745,0.0005194061,0.001759887,0.0000105299,0.000007459173,0.00004079462,0.0004259158,0.3612207,0.5007823,0.004772643,0.1297229,0.0002064092],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8870062,0.00006974517,0.04807767,0.03635587,0.000647173,0.001450046,0.00002035412,0.0003358548,0.02603713],"genre_scores_gemma":[0.983733,0.00000776026,0.0009436302,0.01440389,0.0001114494,0.000005389409,0.000001851934,0.000003249158,0.0007897578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.442962,"threshold_uncertainty_score":0.1665566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04396513450232653,"score_gpt":0.3083906622370362,"score_spread":0.2644255277347096,"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."}}