{"id":"W2983906186","doi":"10.1080/22020586.2019.12073020","title":"Orogenic gold prospectivity mapping using machine learning","year":2019,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital; BC Hydro (Canada); Geoscience BC; University of British Columbia; Western Forest Products","funders":"","keywords":"Prospectivity mapping; Mineral exploration; Metallogeny; Geology; Mineralization (soil science); Artificial intelligence; Mining engineering; Computer science; Data science; Earth science; Geochemistry; Paleontology","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":[0.0005542198,0.0002288444,0.000246947,0.00007715522,0.0001524573,0.0001515424,0.0006089303,0.0001230472,0.00008572035],"category_scores_gemma":[0.0001886157,0.0002231562,0.0001090956,0.0003635177,0.00003170031,0.0005330275,0.0003281498,0.0005601554,0.000358079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006847055,"about_ca_system_score_gemma":0.000099736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001045929,"about_ca_topic_score_gemma":0.000009999692,"domain_scores_codex":[0.9981694,0.00005860093,0.0003072264,0.0006359496,0.0002951733,0.00053363],"domain_scores_gemma":[0.9988246,0.0001018791,0.0002342217,0.0006062381,0.0001001396,0.0001329514],"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":[0.0000628891,0.001092968,0.03155069,0.0008156071,0.0003577914,0.001002955,0.002809125,0.07176801,0.5727663,0.05290981,0.0003974199,0.2644665],"study_design_scores_gemma":[0.001891845,0.0002101199,0.4252599,0.000261517,0.00002982426,0.000875501,0.0002648397,0.3532619,0.1433761,0.0203401,0.05254022,0.001688082],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9558979,0.000229801,0.007483161,0.0006715627,0.0004171473,0.0003101206,0.000002117558,0.000459807,0.03452843],"genre_scores_gemma":[0.9851041,0.000006193467,0.01090125,0.0001169916,0.00007481711,0.000005480602,0.000008633223,0.000006947094,0.003775544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4293902,"threshold_uncertainty_score":0.9100044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759796733790019,"score_gpt":0.2288176581223335,"score_spread":0.2112196907844333,"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."}}