{"id":"W2125691060","doi":"10.1016/j.atmosenv.2006.10.005","title":"Modeling of mercury emission, transport and deposition in North America","year":2006,"lang":"en","type":"article","venue":"Atmospheric Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Environment","keywords":"Mercury (programming language); Tonne; Environmental science; Emission inventory; CMAQ; Atmospheric sciences; Environmental chemistry; Meteorology; Chemistry; Ozone; Air quality index; Geography; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0000546385,0.0001116659,0.0001499042,0.000002405336,0.00005163881,0.000002469875,0.00005084446,0.00002923223,0.0004254426],"category_scores_gemma":[0.000002135776,0.0001015589,0.00002786684,0.000114921,0.000124106,0.0001017255,0.0000377531,0.00005218055,0.00002283509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007194659,"about_ca_system_score_gemma":0.000002516604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001759004,"about_ca_topic_score_gemma":0.0001928381,"domain_scores_codex":[0.9991552,0.00002029042,0.0002659539,0.0001969333,0.0002015434,0.0001600283],"domain_scores_gemma":[0.9997664,0.00001140854,0.00005578807,0.0001155705,0.000001105578,0.00004968961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001004456,0.00009351902,0.7627534,0.000007146847,0.000004751676,0.000003993507,0.0005294605,0.2286902,0.003111484,0.000003127431,0.00006603252,0.004726887],"study_design_scores_gemma":[0.0003771727,0.00006909839,0.9344317,0.00001363379,0.00002480293,0.000003503283,0.000339677,0.0625844,0.0004518019,0.000107418,0.001383813,0.0002129744],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894597,0.0003036104,0.008175587,0.00008140274,0.00001339654,0.0001272798,0.000002857125,0.000009814369,0.001826387],"genre_scores_gemma":[0.9931142,0.0004445388,0.006183047,0.00006430555,0.000008896726,0.00001553105,0.00001252007,0.000008730707,0.0001482666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1716783,"threshold_uncertainty_score":0.4658299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005906443516088287,"score_gpt":0.1934616660256203,"score_spread":0.187555222509532,"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."}}