{"id":"W2169251207","doi":"10.4319/lo.2004.49.6.2276","title":"Thiols in wetland interstitial waters and their role in mercury and methylmercury speciation","year":2004,"lang":"en","type":"article","venue":"Limnology and Oceanography","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta","keywords":"Methylmercury; Mercury (programming language); Wetland; Environmental chemistry; Genetic algorithm; Environmental science; Chemistry; Ecology; Biology; Bioaccumulation","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":[0.00007583111,0.0001830902,0.0001114264,0.0003782378,0.0004997932,0.0004162095,0.0001850102,0.0002218447,0.0002279946],"category_scores_gemma":[0.0001401706,0.0001211837,0.00009685663,0.0002499605,0.0003662354,0.0001908847,0.0002174169,0.000122111,0.00004657662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009051338,"about_ca_system_score_gemma":0.0008074198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1706871,"about_ca_topic_score_gemma":0.2460405,"domain_scores_codex":[0.9999434,0.000004902578,0.000002262824,0.00001391633,0.00001330424,0.00002223121],"domain_scores_gemma":[0.9999313,0.000008703835,0.00001782953,0.000002644963,0.00002248441,0.00001685611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003519777,0.00003918983,0.288847,0.00008357093,0.00003666754,0.0002155441,0.00110893,0.0007915012,0.6989383,0.0001992089,0.00005989445,0.009328303],"study_design_scores_gemma":[0.00001253289,0.0001640603,0.9247354,0.000007803007,0.00003923619,0.0001832952,0.001288125,0.002022328,0.07069519,0.0001518456,0.0006834159,0.00001672081],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997694,0.00003810591,0.00005540338,0.000004622846,2.730119e-7,0.000001020333,0.00002700019,0.000002136599,0.0001020971],"genre_scores_gemma":[0.9994431,0.0000653431,0.0002603311,0.000004345156,5.842778e-7,0.000001560851,0.00005292493,0.000001147513,0.0001705621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1706871,"threshold_uncertainty_score":0.3393872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00678418858003028,"score_gpt":0.2139804930776847,"score_spread":0.2071963044976544,"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."}}