{"id":"W2018336295","doi":"10.3997/1873-0604.2010033","title":"Automation of the SLUTH method: a novel approach to airborne magnetic data interpretation","year":2010,"lang":"en","type":"article","venue":"Near Surface Geophysics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University; Carleton University","funders":"","keywords":"Homogeneity (statistics); Geology; Distortion (music); Regional geology; Economic geology; Geodesy; Geometry; Magnetic field; Homogeneous; Magnetic anomaly; Field (mathematics); Geophysics; Hydrogeology; Mathematics; Physics; Statistics; Statistical physics; Geotechnical engineering; Telmatology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004833563,0.0001397401,0.0002053417,0.00001489644,0.0001191751,0.00005735346,0.0007711843,0.00007416822,0.0001355317],"category_scores_gemma":[0.0002280192,0.00009324629,0.00006475457,0.0008007954,0.00007973971,0.000194666,0.0000974122,0.0003231955,0.0002126095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001851029,"about_ca_system_score_gemma":0.00005586434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00142022,"about_ca_topic_score_gemma":0.0001275504,"domain_scores_codex":[0.9986737,0.0001459737,0.0002201459,0.0003590075,0.0003527633,0.0002484402],"domain_scores_gemma":[0.998625,0.0003097436,0.0001055666,0.0007780449,0.00007774248,0.0001038923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006215855,0.0002126669,0.002869501,0.0000573635,0.00002351839,2.000303e-7,0.0009857008,0.0379841,0.01914963,0.001116044,0.0003190483,0.93722],"study_design_scores_gemma":[0.0001009471,0.00008235787,0.3969818,0.000005404385,0.0000227301,0.000001248754,0.00002168436,0.5991862,0.0008708895,0.001672008,0.0009478772,0.0001068129],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9232169,0.00002230445,0.07153815,0.0003094691,0.0004800801,0.0003507177,0.0001738175,0.00004106762,0.003867435],"genre_scores_gemma":[0.700385,6.071531e-7,0.2991253,0.0001528595,0.00006326459,7.429667e-7,0.00006240156,0.000003908332,0.0002058738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9371132,"threshold_uncertainty_score":0.3802474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246518001558722,"score_gpt":0.2615860066606144,"score_spread":0.2391208266450272,"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."}}