{"id":"W4366224453","doi":"10.1101/2023.04.15.537017","title":"Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Gruppo Nazionale per il Calcolo Scientifico; Centre National de la Recherche Scientifique; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Regularization (linguistics); Computer science; False positives and false negatives; Algorithm; Source code; Mathematical optimization; Mathematics; False positive paradox; Artificial intelligence","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":[0.003927356,0.001036927,0.0006665753,0.0007792353,0.000547154,0.001044331,0.001325431,0.001894396,0.001852928],"category_scores_gemma":[0.02972397,0.0004309127,0.0005784321,0.0005256452,0.001057554,0.001604128,0.0012026,0.001977171,0.0006641406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008406959,"about_ca_system_score_gemma":0.001533356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004385206,"about_ca_topic_score_gemma":0.00490242,"domain_scores_codex":[0.998971,0.0005066757,0.00007142966,0.0001585717,0.0001912846,0.0001011321],"domain_scores_gemma":[0.9941198,0.004335703,0.0003558053,0.0003694923,0.0006932575,0.0001260475],"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":[0.000537272,0.000425809,0.006506635,0.0005647343,0.0002126848,0.0003448921,0.0004112912,0.8279252,0.03592974,0.02734657,0.00810584,0.09168921],"study_design_scores_gemma":[0.00004940753,0.00005785665,0.0008472878,0.00008637363,0.00002112297,0.00006814664,0.00005177198,0.9802474,0.007821244,0.009400542,0.001317558,0.00003132437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1103263,0.0008076348,0.8807392,0.001189674,0.0001410575,0.0001732776,0.0002518452,0.001630929,0.004740068],"genre_scores_gemma":[0.6543123,0.0002694641,0.3427591,0.0003638618,0.00004328519,0.0003804829,0.0003661253,0.0005583033,0.0009471045],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004385206,"threshold_uncertainty_score":0.02077007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04977243076603522,"score_gpt":0.2594069001589474,"score_spread":0.2096344693929122,"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."}}