{"id":"W4416393870","doi":"10.32604/cmc.2025.066421","title":"Error Analysis of Geomagnetic Field Reconstruction Model Using Negative Learning for Seismic Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ; Universiti Putra Malaysia; Sveriges Geologiska Undersökning; Florida Institute of Technology; Alberta Agricultural Research Institute","keywords":"Earth's magnetic field; Sensitivity (control systems); Anomaly detection; Anomaly (physics); Field (mathematics); Pattern recognition (psychology); Magnetic anomaly","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001469995,0.0009389307,0.0005875144,0.0003745195,0.0002384608,0.0006755579,0.001007237,0.0006952103,0.00124901],"category_scores_gemma":[0.005215918,0.0002594534,0.0003923568,0.0001970296,0.0006047756,0.0008368993,0.0009622959,0.001163559,0.0002746093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004495547,"about_ca_system_score_gemma":0.000774461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003713571,"about_ca_topic_score_gemma":0.003590682,"domain_scores_codex":[0.9996728,0.00007070038,0.00002102277,0.00009326543,0.0001002028,0.00004205579],"domain_scores_gemma":[0.9987592,0.0006462093,0.0001491273,0.00009603725,0.0003047625,0.00004466841],"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":[0.0003673933,0.0001164979,0.006363418,0.0001598355,0.00007648572,0.0002040931,0.0001158614,0.8171517,0.01628618,0.00537313,0.001107128,0.1526782],"study_design_scores_gemma":[0.000001840449,0.00002320519,0.0002669848,0.000003331065,0.000003127003,0.00001718457,0.000004233192,0.9973481,0.001664721,0.0005841627,0.00007979672,0.000003369945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1563314,0.0003513246,0.8400857,0.000361091,0.00008408823,0.00003117969,0.00007433595,0.0008367649,0.001844056],"genre_scores_gemma":[0.9590343,0.0001127701,0.03903377,0.00009092691,0.00001778107,0.0000312838,0.000153136,0.00005002132,0.00147597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003713571,"threshold_uncertainty_score":0.007774174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01350375723359049,"score_gpt":0.2346721003626365,"score_spread":0.221168343129046,"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."}}