{"id":"W3133202221","doi":"10.1101/2021.02.16.431436","title":"Input–Output Slope Curve Estimation in Neural Stimulation Based on Optimal Sampling Principles","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Transcranial Magnetic Stimulation Studies","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Percentile; Sampling (signal processing); Value (mathematics); Statistics; Mathematics; Sampling time; Standard deviation; Algorithm; Computer science; Mathematical optimization; Control theory (sociology); Applied mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001202219,0.0003959819,0.0005112053,0.0005295307,0.0001591809,0.0004258079,0.0004421405,0.0004091187,0.0007326545],"category_scores_gemma":[0.006217113,0.0002690351,0.0003235648,0.0004385551,0.000468856,0.000879488,0.0005495722,0.000381377,0.0001478744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000295301,"about_ca_system_score_gemma":0.0004412096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0012406,"about_ca_topic_score_gemma":0.001193187,"domain_scores_codex":[0.999366,0.0002213051,0.00004788278,0.00008886119,0.0002360182,0.00003996298],"domain_scores_gemma":[0.9983191,0.001079486,0.0001200398,0.000141127,0.0003140485,0.00002614074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004187009,0.00007975668,0.005478866,0.0004186422,0.00009760146,0.000160317,0.0002406494,0.4915992,0.07015833,0.007979092,0.0006598854,0.4227089],"study_design_scores_gemma":[0.00001046745,0.00005973136,0.001663537,0.00001671391,0.00001097688,0.00008871576,0.00001411754,0.9810254,0.01446629,0.002175209,0.0004544455,0.00001439579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03335016,0.0002612589,0.9654357,0.00005477078,0.000008653212,0.00002940881,0.0000187389,0.0002082479,0.000633031],"genre_scores_gemma":[0.7672201,0.0002026723,0.231632,0.00005646389,0.00001440378,0.00007517108,0.00007050706,0.00006464332,0.0006639688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0012406,"threshold_uncertainty_score":0.006358027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06184789451157286,"score_gpt":0.2780627704099741,"score_spread":0.2162148758984012,"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."}}