{"id":"W4220975194","doi":"10.1016/j.cageo.2022.105102","title":"MTH5: An archive and exchangeable data format for magnetotelluric time series data","year":2022,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"U.S. Geological Survey; Incorporated Research Institutions for Seismology; National Science Foundation","keywords":"Magnetotellurics; Metadata; Computer science; Interoperability; Workflow; Data element; Python (programming language); Software; Database; Data file; Information retrieval; World Wide Web; Programming language; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0009589164,0.0001368166,0.0001953713,0.0000855583,0.0009089331,0.0001992517,0.00233992,0.0000178189,0.00038205],"category_scores_gemma":[0.00005756371,0.0001082415,0.00001960516,0.0004663823,0.0002352182,0.001374941,0.0008646229,0.0001222361,0.00003310253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002019719,"about_ca_system_score_gemma":0.00005861748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008777829,"about_ca_topic_score_gemma":0.0002725617,"domain_scores_codex":[0.9981564,0.0001805399,0.0001649808,0.0007259478,0.0003204617,0.0004517022],"domain_scores_gemma":[0.9985179,0.0004616668,0.0000710544,0.0007414549,0.00001522334,0.0001927027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005548157,0.00004252638,0.001312991,0.00002242042,0.000007155136,0.000005869631,0.0002679548,0.0007440196,0.0000282354,0.00010329,0.007678566,0.9897315],"study_design_scores_gemma":[0.0001375521,0.001123451,0.02274937,0.00000258678,0.00001342248,0.00003670485,0.0001441906,0.8133485,0.000005104358,0.009778507,0.1524647,0.0001958974],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7119929,0.006653327,0.2396988,0.007070609,0.005125321,0.00273352,0.02345838,0.0005659001,0.00270132],"genre_scores_gemma":[0.2675605,0.0003108984,0.6995931,0.003826574,0.00140392,0.00002970663,0.02262679,0.00002129743,0.004627149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9895356,"threshold_uncertainty_score":0.699087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05490520544180739,"score_gpt":0.2609134141394253,"score_spread":0.2060082086976179,"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."}}