{"id":"W4403402036","doi":"10.1101/2024.10.11.617925","title":"FiNNpy 2.0: Fast MEG source reconstruction","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Krembil Foundation; University Health Network","funders":"","keywords":"Computer science","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004234551,0.0006448,0.0006506045,0.0002866738,0.000219926,0.0004682998,0.0006862461,0.000348448,0.0004069799],"category_scores_gemma":[0.00001657958,0.0006825695,0.0003720607,0.0004963457,0.000201747,0.000139653,0.001239627,0.001979081,0.001007101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003504101,"about_ca_system_score_gemma":0.001415635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002226071,"about_ca_topic_score_gemma":5.311551e-7,"domain_scores_codex":[0.9968765,0.0001271267,0.0005605178,0.001213173,0.0004647644,0.0007579331],"domain_scores_gemma":[0.9977866,0.00006487974,0.0002702529,0.001226523,0.0003223147,0.0003294732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001684402,0.0009533294,0.06617093,0.002497018,0.004795942,0.0001279489,0.0003099222,0.0007948497,0.7905678,0.1052199,0.02147881,0.006915194],"study_design_scores_gemma":[0.002956939,0.0000941568,0.01141532,0.003861778,0.001200745,1.1822e-7,0.0002601326,0.1295756,0.7749186,0.004554776,0.06318743,0.007974429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701107,0.000586855,0.024014,0.0002599381,0.002279844,0.0007248489,0.0006070439,0.0005821807,0.0008346076],"genre_scores_gemma":[0.9965977,0.00003290669,0.0007220026,0.00004009134,0.00205163,0.0002133323,0.000001224231,0.0001994072,0.000141718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1287808,"threshold_uncertainty_score":0.9997707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01247521284419635,"score_gpt":0.2350973424623667,"score_spread":0.2226221296181704,"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."}}