{"id":"W2314543830","doi":"10.1021/es401352n","title":"Impact of Closing Canada’s Largest Point-Source of Mercury Emissions on Local Atmospheric Mercury Concentrations","year":2013,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Environment and Protected Areas; Environment and Climate Change Canada","funders":"","keywords":"Mercury (programming language); Environmental science; Atmospheric emissions; Closing (real estate); Environmental chemistry; Atmospheric sciences; Chemistry; Geology; Political science; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000383708,0.0003634887,0.0002631254,0.000389773,0.00139552,0.0009677298,0.0005421214,0.0003897528,0.001039568],"category_scores_gemma":[0.0007295402,0.0001266538,0.0003359862,0.00060982,0.0005955203,0.0002626507,0.0005866871,0.0003859834,0.0001086685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01380698,"about_ca_system_score_gemma":0.01149402,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.945376,"about_ca_topic_score_gemma":0.9782348,"domain_scores_codex":[0.9995738,0.00003163443,0.000009298741,0.00006603402,0.0001860314,0.0001332876],"domain_scores_gemma":[0.9992651,0.00006216589,0.0001009626,0.0000240187,0.0004021645,0.0001455436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001048728,0.0003015784,0.8824449,0.0002193513,0.0002725698,0.001113166,0.001641078,0.005192619,0.05116612,0.0003824958,0.002759306,0.05345814],"study_design_scores_gemma":[0.000008546777,0.0001923512,0.990315,0.00001903662,0.00004243778,0.00004353474,0.001493323,0.000628149,0.004491474,0.00002518231,0.002730394,0.00001062411],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99722,0.0002558318,0.00008314574,0.0001361087,0.00000790572,0.00001460918,0.0002943123,0.00001885706,0.001969049],"genre_scores_gemma":[0.997173,0.0003210494,0.0002891056,0.00007859466,0.000004322727,0.00000805893,0.0004442028,0.000006111949,0.001675568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05462396,"threshold_uncertainty_score":0.1098912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00574423442679581,"score_gpt":0.2312622434794983,"score_spread":0.2255180090527025,"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."}}