{"id":"W2047940538","doi":"10.1016/j.yebeh.2014.01.010","title":"Advantages of sentence-level fMRI language tasks in presurgical language mapping for temporal lobe epilepsy","year":2014,"lang":"en","type":"article","venue":"Epilepsy & Behavior","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Toronto","funders":"University Health Network; Ontario Brain Institute","keywords":"Epilepsy; Temporal lobe; Sentence; Psychology; Computer science; Natural language processing; Audiology; Artificial intelligence; Neuroscience; Cognitive psychology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001678284,0.0004218362,0.0002146107,0.000307514,0.000206249,0.0006332805,0.0002568355,0.0006150845,0.003143084],"category_scores_gemma":[0.008554726,0.0001285764,0.0002250965,0.0001798777,0.0001713066,0.001431893,0.0002481331,0.0004184479,0.0004719943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200084,"about_ca_system_score_gemma":0.0003695065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006565862,"about_ca_topic_score_gemma":0.003180366,"domain_scores_codex":[0.9997289,0.0001461784,0.00002467677,0.0000339767,0.00004475329,0.0000214569],"domain_scores_gemma":[0.9969774,0.002500654,0.0001096374,0.0001098413,0.0001690836,0.0001333331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.02115772,0.001098861,0.06238453,0.001164174,0.0003788448,0.001155437,0.0008140439,0.003339834,0.47659,0.00273509,0.00503397,0.4241475],"study_design_scores_gemma":[0.001585872,0.01040634,0.685365,0.0003371927,0.001725978,0.009868181,0.001155337,0.07025941,0.1803558,0.02562382,0.01308605,0.0002310436],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712572,0.001586754,0.01323653,0.001088543,0.00019454,0.0001042688,0.0007650194,0.0002152544,0.01155203],"genre_scores_gemma":[0.9886029,0.0005566842,0.008628101,0.0004281315,0.0002079536,0.00005712292,0.0004710152,0.0001072143,0.0009407614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003143084,"threshold_uncertainty_score":0.01051462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03777419762199714,"score_gpt":0.3453867398097837,"score_spread":0.3076125421877866,"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."}}