{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008118447,0.0008509058,0.001532915,0.0005560293,0.0006561896,0.0009651631,0.001582081,0.002196164,0.003066097],"category_scores_gemma":[0.002821604,0.0006669833,0.000729455,0.0009892334,0.0007920223,0.001908802,0.00223816,0.001176318,0.0005101127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002316829,"about_ca_system_score_gemma":0.0007037517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002590756,"about_ca_topic_score_gemma":0.004206013,"domain_scores_codex":[0.9995973,0.00009973584,0.00003153322,0.00009485336,0.0001309162,0.00004565103],"domain_scores_gemma":[0.998942,0.0003986324,0.0001148758,0.0002957295,0.0001638349,0.00008492806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006614696,0.0002325099,0.00785925,0.0004441676,0.0003011258,0.001526515,0.0004290487,0.5050401,0.05014595,0.01122201,0.01145647,0.4106814],"study_design_scores_gemma":[0.00004345092,0.00006673732,0.0009317037,0.000007833904,0.00003194512,0.0002010158,0.0001021115,0.9798655,0.007519701,0.009626521,0.001585077,0.00001828901],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08078833,0.0002360217,0.915499,0.0005939552,0.000104674,0.00003334366,0.0002022525,0.001251219,0.001291137],"genre_scores_gemma":[0.5465447,0.0002853746,0.4476246,0.0002472844,0.0001068013,0.00005226621,0.0004348862,0.0002159681,0.004488225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003066097,"threshold_uncertainty_score":0.01025712,"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."}}