{"id":"W2920897182","doi":"10.1016/j.jglr.2019.03.001","title":"Assessment of mercury mobilization potential in Upper St. Lawrence River riparian wetlands under new water level regulation management","year":2019,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"New York State Water Resources Institute, Cornell University; New York Sea Grant, State University of New York; Great Lakes Research Consortium; National Science Foundation","keywords":"Riparian zone; Wetland; Mobilization; Mercury (programming language); Environmental science; Hydrology (agriculture); River management; STREAMS; Bank; Ecology; Environmental resource management; Geography; Geology; Geomorphology; Geotechnical engineering; Habitat; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002312558,0.0001112075,0.0001061939,0.0003648376,0.0003643944,0.0005059986,0.0002211653,0.0001918673,0.0003762419],"category_scores_gemma":[0.0002706445,0.00008219523,0.0001762164,0.0003039655,0.0002589557,0.000212083,0.0002325413,0.0001097021,0.00006296216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001434449,"about_ca_system_score_gemma":0.00116122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1550874,"about_ca_topic_score_gemma":0.3776435,"domain_scores_codex":[0.9998519,0.00002998857,0.000007260842,0.00002621811,0.00004522727,0.0000394057],"domain_scores_gemma":[0.999785,0.00002880215,0.00004968058,0.00001259956,0.00009166276,0.0000322662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001067483,0.0003285937,0.920656,0.00004831836,0.0001415639,0.0003219489,0.000496225,0.004852242,0.04910602,0.0003721065,0.0003847262,0.02222477],"study_design_scores_gemma":[0.000007678324,0.0002278696,0.9907324,0.000003517724,0.00003847082,0.00002680854,0.0003980576,0.003799055,0.004310688,0.00004026854,0.0004089221,0.000006222943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994537,0.000006754619,0.00003741979,0.000006337914,2.681855e-7,0.000003914482,0.00004445236,0.000002714344,0.0004443681],"genre_scores_gemma":[0.9994156,0.00000845853,0.0001229077,0.000005161757,3.307997e-7,0.000003735887,0.00005870634,6.19381e-7,0.0003844635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1550874,"threshold_uncertainty_score":0.3083694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06035430142928093,"score_gpt":0.3539765937918742,"score_spread":0.2936222923625932,"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."}}