{"id":"W2272827023","doi":"10.1071/aseg2003ab067","title":"Smart solution to a sticky problem: in-mine clay mapping using high-resolution geophysics","year":2003,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"High resolution; Geology; Geophysics; Mineralogy; Remote sensing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006434661,0.0002335217,0.0002321957,0.0001495081,0.0001845567,0.0001050321,0.0003897004,0.0001365083,0.00001445625],"category_scores_gemma":[0.0003052401,0.000248265,0.00005892378,0.0007082812,0.00003574651,0.0004476382,0.0001436461,0.0002708945,0.00009774636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443199,"about_ca_system_score_gemma":0.0001505452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003232035,"about_ca_topic_score_gemma":0.00003684951,"domain_scores_codex":[0.9978867,0.00008302193,0.0004547171,0.0006023701,0.000301742,0.0006714931],"domain_scores_gemma":[0.9988944,0.00008272764,0.0001810928,0.0005206825,0.0001450082,0.0001760761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006223036,0.001684694,0.0008766578,0.0004833228,0.0001014395,0.0007308026,0.002728549,0.32718,0.3757495,0.1908093,0.002233027,0.09736057],"study_design_scores_gemma":[0.003108456,0.0003824601,0.5210258,0.0009380374,0.00005113341,0.0004401804,0.0003259428,0.1227932,0.1053175,0.2138034,0.02919742,0.002616312],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.676963,0.00008317652,0.3032989,0.002199846,0.0006054261,0.0006391627,0.000004733317,0.0002609073,0.01594488],"genre_scores_gemma":[0.8975428,0.000002497829,0.1017712,0.0002424405,0.00006306562,0.00002083596,0.00001087314,0.000005583781,0.0003408022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5201492,"threshold_uncertainty_score":0.999997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02407471164111104,"score_gpt":0.2386112707327949,"score_spread":0.2145365590916838,"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."}}