{"id":"W4381236287","doi":"10.1017/9781009180412.022","title":"Understanding and Predicting Geomagnetic Secular Variation via Data Assimilation","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Geomagnetism and Paleomagnetism Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Earth's magnetic field; Data assimilation; Geomagnetic secular variation; Secular variation; Proxy (statistics); Variation (astronomy); Geophysics; Geology; Climatology; Meteorology; Geomagnetic storm; Geography; Magnetic field; Mathematics; Physics; Astrophysics; Statistics","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":[0.0004215908,0.0005853455,0.0005286151,0.0004600749,0.0001821911,0.00194949,0.0006710722,0.0008835395,0.002843041],"category_scores_gemma":[0.001170895,0.0003029047,0.0004093522,0.001231546,0.000427203,0.00159379,0.000649003,0.00130072,0.00174281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004208295,"about_ca_system_score_gemma":0.0006318378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00883766,"about_ca_topic_score_gemma":0.009735025,"domain_scores_codex":[0.999894,0.00001827448,0.000006432836,0.00003177203,0.00004365372,0.000005836488],"domain_scores_gemma":[0.9998121,0.0001055933,0.0000196524,0.00001987634,0.00003564476,0.000007136598],"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":[0.00005968664,0.00003205007,0.004919006,0.0005490676,0.0001418096,0.0001529813,0.0002567839,0.3809328,0.01349842,0.06639683,0.03201455,0.5010461],"study_design_scores_gemma":[0.000007445526,0.0000198422,0.003738952,0.0001338679,0.00003218147,0.0001259528,0.00006585675,0.8328141,0.004321359,0.07439208,0.08430865,0.00003966729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01925194,0.01623478,0.9295571,0.00330616,0.000737037,0.00003933414,0.001321107,0.002234149,0.0273184],"genre_scores_gemma":[0.2792311,0.0500791,0.6029274,0.0009285314,0.0009142274,0.0001139105,0.005142039,0.001146359,0.05951728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00883766,"threshold_uncertainty_score":0.01757246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06747597186865599,"score_gpt":0.215244239672866,"score_spread":0.14776826780421,"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."}}