{"id":"W4295539201","doi":"10.1186/s40623-022-01695-2","title":"Machine learning-based calibration of the GOCE satellite platform magnetometers","year":2022,"lang":"en","type":"article","venue":"Earth Planets and Space","topic":"Geomagnetism and Paleomagnetism Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; Smithsonian Astrophysical Observatory","keywords":"Magnetometer; Earth's magnetic field; Calibration; Remote sensing; Satellite; Geodesy; Geophysics; Residual; Gravitational field; Payload (computing); Computer science; Space research; Geology; Magnetic field; Aerospace engineering; Physics; Algorithm; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001061223,0.0001003471,0.00009742524,0.00002471923,0.0001910583,0.00001158841,0.00009513931,0.00003526786,0.0001013267],"category_scores_gemma":[0.00001571416,0.00007740239,0.00003652262,0.00007384227,0.00007325574,0.000002425823,0.0001184606,0.0001112322,8.423035e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001328705,"about_ca_system_score_gemma":0.00002488231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001181089,"about_ca_topic_score_gemma":0.0002566249,"domain_scores_codex":[0.9993802,0.00006552619,0.0001055568,0.0001709875,0.0001366083,0.0001410666],"domain_scores_gemma":[0.9997144,0.00001858859,0.00007297265,0.0001492662,0.00001102293,0.00003375217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009475123,0.0002113693,0.7194846,0.000248877,0.0001626979,0.00003103214,0.001476323,0.03653546,0.2058959,0.0005839278,0.009619894,0.02480244],"study_design_scores_gemma":[0.00130277,0.002381851,0.2006928,0.000009985282,0.00003829123,0.00004700996,0.0002803889,0.006134879,0.01284509,0.00003221264,0.7758956,0.0003391235],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871427,0.009261644,0.00007224869,0.0007911061,0.0001565287,0.0001921317,0.0001370161,0.00001081173,0.002235797],"genre_scores_gemma":[0.9939856,0.0004158345,0.0001200919,0.0002050156,0.00002912612,0.000008293307,0.0002041684,0.000008031594,0.005023837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7662757,"threshold_uncertainty_score":0.3156378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007151686437686703,"score_gpt":0.1899736257695837,"score_spread":0.182821939331897,"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."}}