{"id":"W4408081429","doi":"10.1016/j.watres.2025.123428","title":"Transport and transformation of colloidal and particulate mercury in contaminated watershed","year":2025,"lang":"en","type":"article","venue":"Water Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"National Key Research and Development Program of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Guizhou Science and Technology Department; Ministry of Science and Technology of the People's Republic of China","keywords":"Particulates; Mercury (programming language); Contamination; Environmental chemistry; Environmental science; Watershed; Mercury contamination; Environmental engineering; Chemistry; Ecology; Biology","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":[0.0001523953,0.0001403614,0.0001534403,0.0004092462,0.0005086809,0.0007607474,0.0002467741,0.0002762137,0.001025716],"category_scores_gemma":[0.000267966,0.0001469365,0.0001890913,0.0004590201,0.0002817358,0.0004114749,0.0002748406,0.0002772702,0.0002342673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039094,"about_ca_system_score_gemma":0.000767924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05445947,"about_ca_topic_score_gemma":0.02212592,"domain_scores_codex":[0.9998596,0.00002061395,0.000008586439,0.00004314863,0.00003842586,0.00002964044],"domain_scores_gemma":[0.9999273,0.0000176828,0.00001353344,0.000004592358,0.00002445538,0.00001238483],"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":[0.002206901,0.0002984152,0.1279708,0.0001401708,0.00009377313,0.0007019346,0.001287117,0.003647219,0.8446069,0.001734142,0.0004345576,0.01687804],"study_design_scores_gemma":[0.00009925258,0.001281297,0.4586319,0.00002468965,0.0001028114,0.000667521,0.004531342,0.02598391,0.5013542,0.001450382,0.005827468,0.00004531136],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990532,0.000056303,0.0002174971,0.00001509271,0.000002090715,0.000004070839,0.00008023261,0.000004160506,0.0005672454],"genre_scores_gemma":[0.996142,0.0001714823,0.000406969,0.000009725406,0.000003180326,0.000006166053,0.0002072099,0.000009528171,0.003043819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05445947,"threshold_uncertainty_score":0.108285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03269532087465144,"score_gpt":0.3247343509293475,"score_spread":0.2920390300546961,"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."}}