{"id":"W3098137318","doi":"10.1016/j.jes.2020.11.002","title":"Reduction of mercury emissions from anthropogenic sources including coal combustion","year":2020,"lang":"en","type":"article","venue":"Journal of Environmental Sciences","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Health; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mercury (programming language); Environmental science; Coal; Combustion; Coal combustion products; Waste management; Environmental chemistry; Chemistry; Engineering; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003245767,0.0001055795,0.0002054493,0.00003548905,0.0003103227,0.00001995064,0.0002504954,0.00003736596,0.003406068],"category_scores_gemma":[0.0000649526,0.00007928768,0.00009911592,0.0001937768,0.001075997,0.0005674697,0.0001703858,0.0001208789,0.00005884898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008059692,"about_ca_system_score_gemma":0.00001399594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006139818,"about_ca_topic_score_gemma":0.000002081688,"domain_scores_codex":[0.9985433,0.00007070406,0.0004283283,0.00016008,0.0006468119,0.0001508341],"domain_scores_gemma":[0.9991937,0.00005954432,0.0005102398,0.00005720088,0.000003575569,0.0001756872],"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":[0.00004091171,0.0001062591,0.2241059,0.000003099158,0.00002963856,0.000003337201,0.003597612,0.002962336,0.7626357,0.000006573714,0.001733794,0.004774848],"study_design_scores_gemma":[0.001090888,0.001540131,0.6553788,0.00008884788,0.0001569138,0.0000818433,0.02250917,0.0007158222,0.3123947,0.0007136251,0.004954628,0.0003746657],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973637,0.0003901883,0.0001626107,0.001226424,0.0001584928,0.00005797148,0.00001374663,0.000005333287,0.0006215377],"genre_scores_gemma":[0.9984414,0.0004128696,0.000888023,0.0001078286,0.000122948,4.706989e-7,0.000001441055,0.000004463145,0.00002054547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.450241,"threshold_uncertainty_score":0.9975049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04601817679122915,"score_gpt":0.291075039037029,"score_spread":0.2450568622457999,"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."}}