{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00149384,0.0007984537,0.0004469693,0.001361045,0.0002800865,0.0005929891,0.001092135,0.0006752078,0.00211576],"category_scores_gemma":[0.004934934,0.0001800277,0.0004747343,0.00155375,0.000250466,0.0004911836,0.0006991763,0.00110748,0.001563201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006545837,"about_ca_system_score_gemma":0.0007321865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0120396,"about_ca_topic_score_gemma":0.01445271,"domain_scores_codex":[0.9991783,0.0001610962,0.00003463185,0.000291286,0.0002533384,0.00008125848],"domain_scores_gemma":[0.9983516,0.0002776737,0.0001810196,0.0003563082,0.0007882981,0.0000450921],"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":[0.0005717386,0.0002757789,0.0628939,0.0004978619,0.0005620448,0.0002369587,0.00007912787,0.5810459,0.01883466,0.001890693,0.04815694,0.2849544],"study_design_scores_gemma":[0.0001288742,0.0001302033,0.07001618,0.0000912801,0.00008474902,0.00008707816,0.00007014732,0.8714048,0.03234296,0.001964143,0.02359908,0.00008044325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6932623,0.001883696,0.2125618,0.001447824,0.001072365,0.000411516,0.05696625,0.01698012,0.01541417],"genre_scores_gemma":[0.856934,0.0003097116,0.07325631,0.0001595725,0.00009683421,0.0001687408,0.06552985,0.000386173,0.003158794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0120396,"threshold_uncertainty_score":0.02393907,"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."}}