{"id":"W2974230308","doi":"10.1016/j.scitotenv.2019.134472","title":"Periphyton bioconcentrates pesticides downstream of catchment dominated by agricultural land use","year":2019,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph; Trent University; Ministry of Natural Resources and Forestry; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Periphyton; Bioconcentration; Pesticide; Environmental science; Marsh; Wetland; Tributary; Ecosystem; Aquatic ecosystem; Hydrology (agriculture); Environmental chemistry; Bioaccumulation; Ecology; Chemistry; Biomass (ecology); Biology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002871845,0.0001486085,0.000152181,0.00001314547,0.0001517959,0.00002884792,0.000714467,0.00002976207,0.0001877394],"category_scores_gemma":[0.00001526077,0.00006840801,0.00007846297,0.0002504007,0.001905102,0.0002560327,0.000544303,0.00008298386,0.00009902576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001788729,"about_ca_system_score_gemma":0.00000965949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008782157,"about_ca_topic_score_gemma":9.188697e-7,"domain_scores_codex":[0.9984601,0.00004348649,0.0002333885,0.0002815861,0.000673911,0.0003074906],"domain_scores_gemma":[0.9992146,0.00004521093,0.0001886354,0.0004790825,0.000005340703,0.00006709689],"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":[0.00004239235,0.0002865683,0.2617159,0.0000103543,0.00001848914,3.295704e-7,0.001303218,0.1046271,0.6312383,0.00006659636,0.0002786092,0.0004122439],"study_design_scores_gemma":[0.0004149569,0.000168816,0.6293334,0.00002892896,0.0000355856,0.000009488342,0.0004949784,0.003830478,0.3650903,0.0003256996,0.00007902119,0.0001883767],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983993,0.00003249304,0.000002840329,0.0004340318,0.0001572174,0.0003931495,0.00002520883,0.00000706142,0.0005487138],"genre_scores_gemma":[0.9988238,0.00002873575,0.0000759941,0.00001488328,0.000006082151,0.000007419402,0.000003008795,0.000005073479,0.001034989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3676175,"threshold_uncertainty_score":0.7019427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003822828286043256,"score_gpt":0.1729794187408706,"score_spread":0.1691565904548273,"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."}}