{"id":"W1965886581","doi":"10.1016/j.fuel.2010.04.004","title":"Hg occurrence in coal and its removal before coal utilization","year":2010,"lang":"en","type":"article","venue":"Fuel","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National High-tech Research and Development Program; Project 211; Ministry of Science and Technology of the People's Republic of China; Ministry of Science and Technology, Taiwan; National Natural Science Foundation of China","keywords":"Anthracite; Coal; Pyrite; Flue gas; Mercury (programming language); Bituminous coal; Chemistry; Combustion; Coal combustion products; Pyrolysis; Environmental chemistry; Silicate; Extraction (chemistry); Mineralogy; Environmental science; Waste management; Chromatography; Organic chemistry","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":[0.000108172,0.000060168,0.00006337272,0.00002285251,0.0000532169,0.00001273843,0.00005429464,0.00003628066,0.0007076583],"category_scores_gemma":[0.00006737901,0.00005267288,0.000009118117,0.0001023868,0.00008441576,0.0001566966,0.000068396,0.00009140129,0.0001753739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001187326,"about_ca_system_score_gemma":0.000005234097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003486182,"about_ca_topic_score_gemma":0.0007869113,"domain_scores_codex":[0.9995334,0.00001171984,0.00009033912,0.0001242183,0.000116561,0.0001238003],"domain_scores_gemma":[0.9998345,0.00001480059,0.00002744461,0.00006570631,0.000004183566,0.00005336707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001175313,0.00004434008,0.9703264,0.00001832718,0.000002923121,0.00000998317,0.002822669,0.00001442663,0.008896212,0.0008983391,0.003402121,0.01355247],"study_design_scores_gemma":[0.0002631586,0.00002971586,0.9642172,0.000006484269,0.000003394808,0.00001967412,0.0001413064,0.0007578784,0.0008827565,0.0006322418,0.03295212,0.00009409607],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874948,0.00006622452,0.00002742142,0.0001619469,0.0001964367,0.00008643805,0.00001620579,0.00001456762,0.01193592],"genre_scores_gemma":[0.9992273,0.00003485884,0.0001103206,0.0001144971,0.00002453277,0.000003252871,0.000009646041,0.000002418515,0.0004732141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02955,"threshold_uncertainty_score":0.7748362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03228592684597159,"score_gpt":0.2892853643017373,"score_spread":0.2569994374557657,"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."}}