{"id":"W4376611701","doi":"10.3390/min13050672","title":"Deployment of XRF Sensors Underground: An Opportunity for Grade Monitoring or Bulk Ore Sorting in Cave Mines","year":2023,"lang":"en","type":"article","venue":"Minerals","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"MineSense Technologies (Canada); University of British Columbia","funders":"Mitacs; Newcrest Mining","keywords":"Cave; Environmental science; Mining engineering; Sampling (signal processing); Sorting; Iron ore; Underground mining (soft rock); Software deployment; Copper ore; Environmental monitoring; Engineering; Computer science; Environmental engineering; Waste management; Copper; Coal mining; Archaeology; Geography; Coal; Metallurgy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004829878,0.0003203279,0.0003407563,0.0004051304,0.000281128,0.0004356675,0.0007877406,0.0005639811,0.0008210251],"category_scores_gemma":[0.0009183327,0.0002731744,0.000221684,0.0004931627,0.0002714836,0.0004969818,0.0004300925,0.0002794778,0.0002830782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003407777,"about_ca_system_score_gemma":0.0004354896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005939982,"about_ca_topic_score_gemma":0.02295965,"domain_scores_codex":[0.9994685,0.00007944494,0.00002161564,0.0001220611,0.0002503745,0.00005805833],"domain_scores_gemma":[0.9992994,0.0001731968,0.0001706326,0.000108949,0.00021052,0.00003738816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007254761,0.0002780748,0.2194676,0.0003315402,0.00006203486,0.0007491719,0.0006203898,0.008551704,0.5917524,0.0001884491,0.0008217872,0.1764515],"study_design_scores_gemma":[0.00007997895,0.002435054,0.4501094,0.0001183857,0.0001003157,0.001501222,0.001429569,0.07948209,0.4564249,0.0002802813,0.007929323,0.0001094717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732265,0.0001008484,0.02416316,0.00005062112,0.000008028803,0.00006453081,0.0002813539,0.0005612834,0.001543666],"genre_scores_gemma":[0.9594187,0.00007175194,0.03912322,0.00002303502,0.000002248553,0.00003362798,0.0001890859,0.00002662736,0.001111698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005939982,"threshold_uncertainty_score":0.01181084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1082131628331074,"score_gpt":0.3294199807669698,"score_spread":0.2212068179338623,"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."}}