{"id":"W2523135885","doi":"10.1190/geo2016-0119.1","title":"Fitting superparamagnetic and distributed Cole-Cole parameters to airborne electromagnetic data: A case history from Quebec","year":2016,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Induced polarization; Superparamagnetism; Polarizability; Permeability (electromagnetism); Conductivity; Polarization (electrochemistry); Electrical conductor; Intrusion; Geology; Materials science; Computational physics; Condensed matter physics; Physics; Magnetization; Electrical resistivity and conductivity; Chemistry; Magnetic field; Quantum mechanics; Geochemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000537481,0.0005163313,0.0002563819,0.0008906365,0.0009461715,0.0006678002,0.001057287,0.0008457925,0.001489213],"category_scores_gemma":[0.00212668,0.0001642299,0.0002631716,0.001858725,0.0004848093,0.0003069548,0.0002648596,0.0004257555,0.0002676124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007091344,"about_ca_system_score_gemma":0.00204023,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9358723,"about_ca_topic_score_gemma":0.9541341,"domain_scores_codex":[0.9995704,0.00007325674,0.00001657412,0.0001186991,0.0001371164,0.00008389433],"domain_scores_gemma":[0.9986058,0.0004006138,0.00008338436,0.000149777,0.0007062402,0.0000542238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003846321,0.0005080354,0.6736136,0.0002075281,0.0002556172,0.00996163,0.002120034,0.1592668,0.03237791,0.001567593,0.00705225,0.1126843],"study_design_scores_gemma":[0.00005934629,0.0002099609,0.7075327,0.00005081582,0.00008334021,0.001582706,0.002852292,0.2615196,0.01271834,0.0005119599,0.01276607,0.0001129316],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910703,0.00007999353,0.003701385,0.0001255516,0.000008315128,0.00004645995,0.001004124,0.0001530729,0.003810859],"genre_scores_gemma":[0.9957513,0.00003392962,0.002268694,0.00002512774,0.000002259776,0.000008291403,0.0005236162,0.00002761646,0.001359097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9358723,"threshold_uncertainty_score":0.1290106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496129526801862,"score_gpt":0.2202085886298164,"score_spread":0.1952472933617978,"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."}}