{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003725734,0.0001967477,0.0003056327,0.00025905,0.00006737183,0.00003800084,0.0001320444,0.0000898218,0.00001235648],"category_scores_gemma":[0.0001624378,0.0001759557,0.00005799293,0.0004212681,0.00001986322,0.0001818816,0.00002958575,0.0001042055,0.000002522462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006954673,"about_ca_system_score_gemma":0.00002687547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002132018,"about_ca_topic_score_gemma":0.0002204588,"domain_scores_codex":[0.9987197,0.00002568693,0.0004596264,0.000213339,0.0001618275,0.000419808],"domain_scores_gemma":[0.9994396,0.0001465766,0.00007738061,0.0001715365,0.00004031116,0.0001246224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006279645,0.000119758,0.02256286,0.001817802,0.000100803,0.000106799,0.004744689,0.3861957,0.5724763,0.0002335859,0.003986455,0.00759244],"study_design_scores_gemma":[0.004364023,0.0005770191,0.01055762,0.002074577,0.000125176,0.00006396654,0.01252939,0.6507167,0.3080842,0.003698997,0.00485942,0.002348877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985022,0.0001026431,0.0001187961,0.00008845839,0.0003448539,0.0001895707,0.00001433536,0.0003226959,0.0003164561],"genre_scores_gemma":[0.9957595,0.00004364148,0.00115941,0.00001006602,0.0002893592,0.00005624473,0.00003326907,0.00006165109,0.002586888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.264521,"threshold_uncertainty_score":0.7175264,"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."}}