{"id":"W4225320112","doi":"10.21203/rs.3.rs-1607576/v1","title":"Machine Learning-based Calibration of the GOCE Satellite Platform Magnetometers","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Resources Canada; Smithsonian Astrophysical Observatory","keywords":"Satellite; Magnetometer; Calibration; Remote sensing; Computer science; Artificial intelligence; Global Positioning System; Geodesy; Computer vision; Geology; Engineering; Aerospace engineering; Physics; Telecommunications","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":[0.0007625929,0.0005491365,0.0004704982,0.0009996917,0.0003733474,0.000695305,0.0008479087,0.000866669,0.001700301],"category_scores_gemma":[0.005959809,0.0002619385,0.0003333528,0.001170217,0.0002662062,0.0005617742,0.0008126589,0.00114367,0.001357052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005818947,"about_ca_system_score_gemma":0.0009454835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006923704,"about_ca_topic_score_gemma":0.0102587,"domain_scores_codex":[0.9994885,0.0001099343,0.0000189727,0.0001709901,0.0001452059,0.00006643179],"domain_scores_gemma":[0.9991031,0.0001956899,0.00009660273,0.0002015814,0.0003727172,0.00003043374],"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":[0.0003258415,0.00008957438,0.0194573,0.000129886,0.0001238426,0.00006305346,0.0001085717,0.4863003,0.03679226,0.005488303,0.006204785,0.4449163],"study_design_scores_gemma":[0.00004022941,0.00002220683,0.01397118,0.00002322144,0.00001798573,0.00004729702,0.00001867239,0.9635803,0.01572069,0.003094201,0.003442251,0.00002181236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3366274,0.0006498904,0.6429263,0.0006392588,0.0003350593,0.00007709576,0.001474828,0.005424344,0.01184579],"genre_scores_gemma":[0.8950537,0.000128944,0.1007339,0.0000799404,0.00005147159,0.00004069893,0.001582463,0.0002226154,0.002106228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006923704,"threshold_uncertainty_score":0.01376683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07870150964668907,"score_gpt":0.3146139306891722,"score_spread":0.2359124210424831,"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."}}