{"id":"W3186526120","doi":"10.1098/rsos.210414","title":"Monitoring moisture content and evaporation kinetics from mine slurries through albedo measurements to help predict and prevent dust emissions","year":2021,"lang":"en","type":"article","venue":"Royal Society Open Science","topic":"Coal and Its By-products","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Ministère de l'Énergie et des Ressources Naturelles; Rio Tinto; Université de Sherbrooke","keywords":"Tailings; Environmental science; Water content; Bauxite; Albedo (alchemy); Slurry; Moisture; Humidity; Soil science; Atmospheric sciences; Environmental engineering; Meteorology; Materials science; Geology; 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":[0.0002822366,0.0002606679,0.0002282367,0.0004246289,0.0001654403,0.000354732,0.0001969674,0.0002374821,0.0005664426],"category_scores_gemma":[0.0003957515,0.0001345363,0.000144237,0.0002484396,0.0001502542,0.0003420014,0.0001480378,0.0002886494,0.0002198337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146721,"about_ca_system_score_gemma":0.0001466096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001323584,"about_ca_topic_score_gemma":0.003882047,"domain_scores_codex":[0.999877,0.00001470745,0.000006056857,0.00003681384,0.00005376541,0.00001165945],"domain_scores_gemma":[0.9998147,0.00005545892,0.00006238227,0.00001444529,0.00004306185,0.00001003247],"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":[0.0001172358,0.00008356322,0.05881778,0.000116348,0.00003337202,0.00005693003,0.00006211419,0.002078866,0.9184359,0.00008753075,0.0001499751,0.01996035],"study_design_scores_gemma":[0.0000113659,0.0003305589,0.1668418,0.00002579548,0.00004991417,0.0001526839,0.0002038203,0.04280549,0.7878339,0.0002627808,0.001453953,0.00002792973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815937,0.0004909505,0.01581674,0.00004963915,0.0000147878,0.00002415088,0.0004297355,0.0001880866,0.001392194],"genre_scores_gemma":[0.989675,0.0003895593,0.00918793,0.00001999164,0.000006676079,0.00001394967,0.0001896563,0.00001795451,0.0004991489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001323584,"threshold_uncertainty_score":0.002631724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07647801944698729,"score_gpt":0.2788125241860831,"score_spread":0.2023345047390958,"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."}}