{"id":"W4308406639","doi":"10.22541/au.166785474.48835866/v1","title":"Adaptively Clock-boosted  Auto-ranging Neural-interface  for Emerging Neuromodulation Applications","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; CMC Microsystems","keywords":"Deep brain stimulation; Neuromodulation; Local field potential; Computer science; Brain–computer interface; Stimulation; Neuroscience; Electroencephalography; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002202603,0.0004918555,0.0003935301,0.0004238614,0.0007315612,0.0002303396,0.001090417,0.0001215547,0.0001539321],"category_scores_gemma":[0.0003161796,0.0005256824,0.0002977793,0.0006881687,0.00007589052,0.0003257534,0.001285293,0.0009174464,0.0000177419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001369945,"about_ca_system_score_gemma":0.0000771078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001999649,"about_ca_topic_score_gemma":0.000001801355,"domain_scores_codex":[0.9964449,0.0001211376,0.0005677654,0.001639705,0.0005488712,0.0006776362],"domain_scores_gemma":[0.9982302,0.0004859976,0.0003060325,0.000771249,0.00005470273,0.000151791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002944287,0.00005555231,0.00001027059,0.0001236714,0.000003879945,0.000009570819,0.0001356643,0.2380674,0.753152,0.003829216,0.0001842524,0.00439905],"study_design_scores_gemma":[0.0002783646,0.00009648911,0.0001208422,0.0000261706,0.00003007679,0.00002744736,0.00004795725,0.7574335,0.2082344,0.0005588898,0.03256728,0.0005786517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3154311,0.0002252734,0.6576537,0.003434734,0.006052415,0.008659305,0.0004845533,0.003328551,0.004730251],"genre_scores_gemma":[0.9926292,0.00004949441,0.001025726,0.001065595,0.0002132916,0.001911158,0.00002870594,0.000124591,0.002952222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6771981,"threshold_uncertainty_score":0.9997195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06873319079686176,"score_gpt":0.3276080827378423,"score_spread":0.2588748919409805,"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."}}