{"id":"W4407013470","doi":"10.1101/2025.01.28.635329","title":"Source Reconstruction Without an MRI using Optically Pumped Magnetometer based Magnetoencephalography","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"Engineering and Physical Sciences Research Council","keywords":"Magnetoencephalography; Magnetometer; Nuclear magnetic resonance; Computer science; Computer vision; Physics; Materials science; Artificial intelligence; Psychology; Magnetic field; Neuroscience; Electroencephalography","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":[0.0005828956,0.0007721655,0.0008476005,0.0006562229,0.0003371442,0.0004658698,0.0009365813,0.0004299452,0.000393343],"category_scores_gemma":[0.00002053385,0.0008520615,0.0003992329,0.0008607616,0.0003661485,0.0003617348,0.0005225319,0.001346232,0.00003556615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002740292,"about_ca_system_score_gemma":0.002573562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003437895,"about_ca_topic_score_gemma":0.000001060441,"domain_scores_codex":[0.996013,0.0003360251,0.000703692,0.001463151,0.0005465217,0.0009375401],"domain_scores_gemma":[0.9967576,0.00009101225,0.0003956632,0.001643728,0.0006314853,0.0004804873],"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":[0.0002972436,0.001072827,0.4520489,0.0008099029,0.001104264,0.00002126065,0.0000649044,0.00395755,0.5350458,0.00453833,0.0001795187,0.0008594874],"study_design_scores_gemma":[0.002894378,0.0001329902,0.02003176,0.001165175,0.0007844669,4.234151e-8,0.00007882821,0.8560738,0.1132947,0.0001843626,0.00183915,0.003520383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7839227,0.00008326632,0.213743,0.00004827591,0.0006651332,0.0007556936,0.0003643589,0.0002543097,0.0001632914],"genre_scores_gemma":[0.9710122,0.00001155037,0.02784085,0.00008640722,0.0007388085,0.00016785,0.000002880472,0.0001132118,0.00002622176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8521162,"threshold_uncertainty_score":0.999393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727322836663199,"score_gpt":0.259546565769963,"score_spread":0.2422733374033311,"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."}}