{"id":"W4320907019","doi":"10.1016/j.brs.2023.01.261","title":"TMS adaptable auditory control - a universal tool to deal with TMS-evoked auditory potential","year":2023,"lang":"en","type":"article","venue":"Brain stimulation","topic":"Transcranial Magnetic Stimulation Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Transcranial magnetic stimulation; Computer science; Electroencephalography; Speech recognition; Masking (illustration); Noise (video); Artificial intelligence; Stimulation; Psychology; Neuroscience","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000255128,0.0002699765,0.0002926263,0.0003286536,0.0004423812,0.00008174764,0.0002184141,0.0001067511,0.0002005095],"category_scores_gemma":[0.0003569217,0.0002629336,0.00008116301,0.001017834,0.0001389692,0.0003447447,0.00006072448,0.0001562083,0.0006946708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170232,"about_ca_system_score_gemma":0.0001216392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005632053,"about_ca_topic_score_gemma":0.00002662777,"domain_scores_codex":[0.9975502,0.0001957421,0.0003238901,0.0006501809,0.0007708539,0.0005090776],"domain_scores_gemma":[0.9983249,0.0009895404,0.0001179131,0.0002835566,0.0001370358,0.0001470583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001199773,0.00006223041,0.000604663,0.00001789398,0.00002395658,0.00007875221,0.0005943896,0.6150749,0.3612301,0.0007220923,0.01573223,0.004658947],"study_design_scores_gemma":[0.009585029,0.0008691012,0.7050167,0.00005622172,0.0000985013,0.00001292234,0.0001714288,0.2267095,0.0004725639,0.000453002,0.05581659,0.0007384578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.909696,0.00001228883,0.08200271,0.003126533,0.001312476,0.001442125,0.00009435241,0.0009885307,0.001325017],"genre_scores_gemma":[0.9942224,0.000002931245,0.00115783,0.0007794727,0.0009154986,0.00005715449,0.00001355417,0.00005045503,0.002800713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.704412,"threshold_uncertainty_score":0.9999823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027468034044363,"score_gpt":0.2510694440296943,"score_spread":0.2307947636892507,"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."}}