{"id":"W4409722573","doi":"10.1080/19236026.2025.2465091","title":"Current state of industry practice in mineral resource estimation and classification","year":2025,"lang":"en","type":"article","venue":"CIM Journal","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimation; State (computer science); Mineral resource classification; Resource (disambiguation); Mineral; Business; Computer science; Geology; Economics; Geochemistry; Management; Materials science; Metallurgy; Algorithm","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":[0.07757083,0.0009863935,0.001243668,0.01425402,0.001337459,0.009268139,0.005431619,0.002192322,0.002917879],"category_scores_gemma":[0.14464,0.0008583828,0.001258192,0.02003348,0.005349969,0.009395266,0.002658616,0.002524117,0.002980079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004972825,"about_ca_system_score_gemma":0.01035865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01421588,"about_ca_topic_score_gemma":0.01212156,"domain_scores_codex":[0.9524525,0.0136476,0.007184512,0.007970151,0.01785055,0.0008947836],"domain_scores_gemma":[0.689876,0.1939208,0.01479124,0.01797971,0.08129146,0.002140725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009245037,0.0001166981,0.01562923,0.005733305,0.0001237429,0.00007913388,0.001699625,0.003624369,0.001008288,0.01369879,0.02348109,0.9347132],"study_design_scores_gemma":[0.00003838768,0.0003432313,0.03725268,0.02465342,0.0003081598,0.0007885825,0.008432397,0.01696113,0.007866116,0.03460132,0.8684773,0.0002773339],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.07126086,0.5597075,0.2211462,0.07351236,0.002883144,0.0003973011,0.00284598,0.002021191,0.06622548],"genre_scores_gemma":[0.2552374,0.4459146,0.271762,0.009642565,0.003473677,0.0004357254,0.005732092,0.0009536119,0.006848329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07757083,"threshold_uncertainty_score":0.4102387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02365601980649655,"score_gpt":0.3020532223411146,"score_spread":0.2783972025346181,"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."}}