{"id":"W2908085908","doi":"10.1109/tgrs.2018.2852632","title":"A Nonlinear Regression Application via Machine Learning Techniques for Geomagnetic Data Reconstruction Processing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Earth's magnetic field; Computer science; Machine learning; Support vector machine; Artificial intelligence; Artificial neural network; Hyperplane; Boosting (machine learning); Interpolation (computer graphics); Algorithm; Data mining; Mathematics; Magnetic field","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":[0.0006762671,0.0006042225,0.0005175814,0.0005462135,0.0002913337,0.0004725903,0.0006138684,0.0006138012,0.001258028],"category_scores_gemma":[0.001647191,0.0002924231,0.0006352264,0.0009256494,0.0002756108,0.0006842972,0.000525408,0.001026054,0.0006584593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002809504,"about_ca_system_score_gemma":0.0007424431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003537508,"about_ca_topic_score_gemma":0.003673514,"domain_scores_codex":[0.9996877,0.00006017706,0.00002291468,0.00008817988,0.0001119699,0.0000291488],"domain_scores_gemma":[0.9996471,0.0001137329,0.00004462391,0.00004810362,0.0001330621,0.00001339023],"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":[0.0001132788,0.0001019175,0.002929566,0.0002165058,0.0000966471,0.0001911918,0.0001529447,0.3286769,0.03566481,0.008873371,0.003502652,0.6194802],"study_design_scores_gemma":[0.0000027657,0.00001800294,0.0002831209,0.000005024819,0.000005943817,0.00003433323,0.000009017586,0.9936718,0.003659947,0.001051157,0.00125299,0.000005830749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007694538,0.0002078682,0.9907048,0.0001016219,0.00003426423,0.00001514787,0.000030196,0.0005915111,0.0006200041],"genre_scores_gemma":[0.3448894,0.0007851713,0.649411,0.0001208665,0.00009217497,0.00009458555,0.0002910632,0.0001294247,0.004186253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003537508,"threshold_uncertainty_score":0.007033825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715024817124112,"score_gpt":0.2601588731059324,"score_spread":0.2430086249346913,"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."}}