{"id":"W4382344834","doi":"10.1021/acs.est.3c01273","title":"Using Mercury Stable Isotopes to Quantify Bidirectional Water–Atmosphere Hg(0) Exchange Fluxes and Explore Controlling Factors","year":2023,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"K. C. Wong Education Foundation; Key Research Program of Frontier Science, Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Surface water; Atmosphere (unit); Environmental chemistry; Deposition (geology); Mercury (programming language); Chemistry; Isotope; Mass-independent fractionation; Water column; Isotope fractionation; Fractionation; Environmental science; Environmental engineering; Meteorology; Geology; Sediment","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003048257,0.0005686897,0.0002658797,0.0005329197,0.0003878422,0.0004219585,0.0002636736,0.0003868544,0.0003201914],"category_scores_gemma":[0.0001854511,0.0002510192,0.0002895835,0.0005345957,0.000250382,0.0004928508,0.0003241962,0.0002104273,0.00008007052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000385015,"about_ca_system_score_gemma":0.0003617963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063431,"about_ca_topic_score_gemma":0.02880225,"domain_scores_codex":[0.9998542,0.00001299432,0.000006859824,0.00005927403,0.00004811592,0.00001842361],"domain_scores_gemma":[0.9999278,0.00001366061,0.00002449722,0.000005623329,0.00002224246,0.000006091493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001182205,0.00003591465,0.2336115,0.000140414,0.0001779982,0.0001011004,0.0002656481,0.0009772395,0.7420903,0.0002076673,0.0001081375,0.02216586],"study_design_scores_gemma":[0.00002783179,0.0002189242,0.726792,0.00001393275,0.0002204364,0.0001340535,0.0003712884,0.01837523,0.2503107,0.0005466953,0.002942695,0.00004637545],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990523,0.0004804188,0.007952589,0.00003011052,0.00001727039,0.00001797817,0.0001872077,0.00005301148,0.0007383985],"genre_scores_gemma":[0.9904684,0.0004052339,0.007946627,0.00005464202,0.00001254507,0.00002513484,0.0002524386,0.00001844684,0.0008164684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01063431,"threshold_uncertainty_score":0.02114481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04580448967269408,"score_gpt":0.2829372520769396,"score_spread":0.2371327624042455,"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."}}