{"id":"W3039340987","doi":"10.1016/j.jhazmat.2020.123384","title":"Mercury isotope signatures of a pre-calciner cement plant in Southwest China","year":2020,"lang":"en","type":"article","venue":"Journal of Hazardous Materials","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"State Key Laboratory of Environmental Geochemistry; K. C. Wong Education Foundation; National Natural Science Foundation of China","keywords":"Fly ash; Cement; Mercury (programming language); Kiln; Flue gas; Cement kiln; Environmental chemistry; Coal; Environmental science; Raw material; Waste management; Chemistry; Mineralogy; Metallurgy; Materials 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009366342,0.0002421144,0.000231249,0.0009053499,0.001131203,0.0003710034,0.000524985,0.0004816979,0.0006640719],"category_scores_gemma":[0.00009210177,0.0002359216,0.0001690614,0.0008689169,0.0004327039,0.0002544702,0.0003239954,0.000182307,0.0001410105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312254,"about_ca_system_score_gemma":0.001299965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1316478,"about_ca_topic_score_gemma":0.2564272,"domain_scores_codex":[0.9999248,0.000004599513,0.000003989207,0.00001940236,0.00002793061,0.00001937001],"domain_scores_gemma":[0.9998547,0.00001632599,0.00003752194,0.000007189491,0.00005241329,0.00003182509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003404796,0.000138787,0.8771206,0.00005578752,0.00005261661,0.002963294,0.003073458,0.00156104,0.1017352,0.0002278245,0.0003576766,0.01237315],"study_design_scores_gemma":[0.000004766245,0.0000414433,0.9934605,0.000002614131,0.00001542291,0.0001682359,0.001306753,0.001439952,0.003115374,0.00003156083,0.0004057346,0.000007569096],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995123,0.000008834465,0.00003918785,0.00001320944,4.580662e-7,0.000001639449,0.00003997494,0.00000398917,0.0003804377],"genre_scores_gemma":[0.9992744,0.00001272091,0.00007095381,0.00000570295,0.000001052669,0.000001144257,0.00004818029,0.000001709354,0.000584072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1316478,"threshold_uncertainty_score":0.2617631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172910033132768,"score_gpt":0.2500466811553428,"score_spread":0.232755677842066,"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."}}