{"id":"W2313377068","doi":"10.1190/segam2012-0079.1","title":"Mitigating remanent magnetization effects in magnetic data using the normalized source strength","year":2012,"lang":"en","type":"article","venue":"","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Remanence; Magnetization; Natural remanent magnetization; Data source; Inversion (geology); Geology; Mineralogy; Magnetic field; Computer science; Physics; Seismology; Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000567447,0.000100042,0.000121542,0.00003568227,0.0001093726,0.00004130748,0.0002910129,0.00003817436,0.0007943608],"category_scores_gemma":[0.0002610867,0.00005849416,0.0000208783,0.0004305006,0.00003799399,0.0002480536,0.00004358011,0.0001404305,0.00007263169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002389909,"about_ca_system_score_gemma":0.00001120768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003482837,"about_ca_topic_score_gemma":0.0004680326,"domain_scores_codex":[0.9987194,0.0003506562,0.0001697222,0.0001765256,0.0001946376,0.000389089],"domain_scores_gemma":[0.9988543,0.0007044607,0.0000432942,0.0002864688,0.00001067251,0.0001007471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001705318,0.00003909998,0.201621,0.00002925283,0.00000331902,0.000001441975,0.0001698987,0.002310396,0.0002791588,0.0001011807,0.00005927476,0.7953689],"study_design_scores_gemma":[0.0001693468,0.00006230985,0.5401463,0.00001151881,0.00001403236,0.00000433019,0.000051283,0.4579999,0.0002306738,0.0003749364,0.000838185,0.00009723515],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894781,0.001044383,0.00645357,0.0001745132,0.0001766773,0.0002231918,0.000005349622,0.00002957503,0.002414624],"genre_scores_gemma":[0.9800099,0.000008323661,0.01922198,0.0002647839,0.0001753351,6.401102e-7,0.00005893581,0.000002482823,0.0002575738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7952716,"threshold_uncertainty_score":0.8697694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02884918945285295,"score_gpt":0.2638520394564746,"score_spread":0.2350028500036216,"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."}}