{"id":"W2039031924","doi":"10.1016/j.jclepro.2015.01.087","title":"Reducing mercury pollution by training Peruvian artisanal gold miners","year":2015,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Mining and Resource Management","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mercury (programming language); Gold cyanidation; Gold mining; Gold ore; Mining engineering; Mercury pollution; Pollution; Environmental science; Engineering; Environmental protection; Geology; Metallurgy; Geochemistry; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000748129,0.0001060712,0.0001604613,0.0001854425,0.00002551581,0.00003219177,0.00008286734,0.0000475054,0.00001081194],"category_scores_gemma":[0.0001014432,0.00009283483,0.00006383094,0.0001406049,0.00002659409,0.0002013893,0.000009140591,0.0002339405,0.000008040775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001404587,"about_ca_system_score_gemma":0.00001954077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001059471,"about_ca_topic_score_gemma":0.00000129612,"domain_scores_codex":[0.999018,0.00003970088,0.0003512091,0.0001080704,0.0003047586,0.000178294],"domain_scores_gemma":[0.9995431,0.000004795343,0.0001346795,0.0001113704,0.00007812082,0.0001279135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003649578,0.00002837731,0.00008093823,0.000035736,0.00009673985,0.00001098762,0.00653264,0.5184546,0.0283605,0.00001206986,0.3668186,0.0795323],"study_design_scores_gemma":[0.001233705,0.0004726227,0.0008404572,0.0003265484,0.0002396325,0.001013052,0.02740754,0.02338803,0.01835625,0.000072841,0.9260843,0.0005649552],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861319,0.0008498559,0.001418405,0.001816488,0.002164697,0.00006686128,8.397852e-7,0.00007350562,0.007477412],"genre_scores_gemma":[0.9950757,0.00004230381,0.0007800333,0.00002811299,0.001449694,0.000001074342,0.000002197129,0.0000239405,0.002596917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5592658,"threshold_uncertainty_score":0.3785695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02546507931474762,"score_gpt":0.2230746616901894,"score_spread":0.1976095823754418,"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."}}