{"id":"W2138371011","doi":"10.3389/fpsyg.2013.00265","title":"Enhancing SART Validity by Statistically Controlling Speed-Accuracy Trade-Offs","year":2013,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Mind wandering and attention","field":"Neuroscience","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Task (project management); Psychology; Rendering (computer graphics); Computer science; Cognitive psychology; Machine learning; 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":[0.0002702162,0.0001613499,0.0002860156,0.0001176675,0.00009810642,0.00005780828,0.0002458468,0.0001308857,0.0003323773],"category_scores_gemma":[0.0006171179,0.0001588652,0.00005466473,0.0001797039,0.0001495511,0.0001900371,0.0000215263,0.0003120572,0.0001460031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003488161,"about_ca_system_score_gemma":0.00001530595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002482227,"about_ca_topic_score_gemma":0.000002745338,"domain_scores_codex":[0.9981643,0.0002592203,0.0003922602,0.0005430141,0.0001662172,0.0004750158],"domain_scores_gemma":[0.9992796,0.0002305763,0.0001138724,0.0002569118,0.00001246974,0.0001065249],"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":[0.00007521097,0.0002109889,0.007739128,0.00001932473,0.000007718807,0.0000302721,0.0002272904,0.00001266136,0.701818,0.0002285806,0.2383283,0.05130255],"study_design_scores_gemma":[0.02097875,0.001941098,0.07522919,0.0003345276,0.0001204459,0.0003329471,0.001456363,0.04013807,0.458426,0.1868461,0.210721,0.003475519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4271472,0.0001558836,0.5634107,0.002055462,0.003658112,0.0003855542,0.00002621715,0.00008465724,0.003076167],"genre_scores_gemma":[0.9881388,0.0001292836,0.008789654,0.002514241,0.0001035945,0.00002584704,0.000008564464,0.00002199187,0.0002680501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5609915,"threshold_uncertainty_score":0.6478333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0373941241539445,"score_gpt":0.3155397332903084,"score_spread":0.2781456091363639,"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."}}