{"id":"W2078672105","doi":"10.1021/es0498540","title":"Comparison between Back-Trajectory Based Modeling and Lagrangian Backward Dispersion Modeling for Locating Sources of Reactive Gaseous Mercury","year":2005,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State University of New York Oswego; State University of New York Fredonia; National Oceanic and Atmospheric Administration; Georgia Institute of Technology; New York State Energy Research and Development Authority","keywords":"Mercury (programming language); Dispersion (optics); Environmental science; Atmospheric dispersion modeling; Lagrangian; Deposition (geology); Environmental chemistry; Atmospheric sciences; Environmental engineering; Chemistry; Meteorology; Mineralogy; Mathematics; Air pollution; Geology; Geography; Structural basin; Physics; Applied mathematics; Computer science; Geomorphology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004858293,0.0002050495,0.0003077561,0.0002237811,0.0004985491,0.0000179797,0.0003449441,0.0001181626,0.000139958],"category_scores_gemma":[0.00005465719,0.0001896832,0.00005762409,0.0004053952,0.001721791,0.0003937634,0.0002946153,0.0001658928,0.0000457622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003139649,"about_ca_system_score_gemma":0.00001663594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007981498,"about_ca_topic_score_gemma":0.00002922592,"domain_scores_codex":[0.998207,0.00002294962,0.0003723331,0.0005316951,0.0003905427,0.0004755008],"domain_scores_gemma":[0.9993917,0.00006744124,0.0001604697,0.0002456586,0.000006597728,0.0001281042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003165275,0.0001547467,0.2996313,0.00001934314,0.0000176924,5.175621e-7,0.001794857,0.2189125,0.4290576,0.0000325837,0.0000273857,0.05031982],"study_design_scores_gemma":[0.0005769841,0.0002483543,0.005588403,0.0000458007,0.00005134396,0.000006546311,0.005365194,0.8461132,0.1409521,0.0002047935,0.0004810709,0.0003662416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678656,0.0002664353,0.030593,0.0004722575,0.00002826242,0.0003471128,0.0000218867,0.00004769725,0.0003577851],"genre_scores_gemma":[0.9869519,0.00003591943,0.01286,0.0000603437,0.00002639349,0.00002079539,0.000007601524,0.00001495879,0.00002204416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6272007,"threshold_uncertainty_score":0.7735055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233390130251529,"score_gpt":0.2746927565444601,"score_spread":0.2513537435193072,"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."}}