{"id":"W3165796398","doi":"10.1021/acs.est.1c02326","title":"Occurrence of Arsenic in Nearshore Aquifers Adjacent to Large Inland Lakes","year":2021,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Arsenic; Aquifer; Pollutant; Sediment; Environmental science; Groundwater; Environmental chemistry; Arsenic contamination of groundwater; Mercury (programming language); Hydrology (agriculture); Geology; Ecology; Chemistry; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007025414,0.0001687258,0.0001773953,0.0008153229,0.0006075077,0.0004076642,0.0001646167,0.000214991,0.0006120819],"category_scores_gemma":[0.0002154234,0.0001825843,0.00008717752,0.0007387192,0.000473406,0.0002647614,0.0003589806,0.0001018697,0.00009151407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006135228,"about_ca_system_score_gemma":0.0003954607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03559071,"about_ca_topic_score_gemma":0.1016176,"domain_scores_codex":[0.9998887,0.00001319403,0.000008832105,0.00003280981,0.00002944125,0.00002709132],"domain_scores_gemma":[0.9998142,0.00001803748,0.00008625282,0.00000724976,0.00004133782,0.00003286912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002183403,0.00004799787,0.9312187,0.00005659303,0.00002652479,0.0005425633,0.002616232,0.0002369397,0.05948073,0.00008101352,0.00009054784,0.005383813],"study_design_scores_gemma":[0.000002765614,0.00008044751,0.996176,0.000003471147,0.00001084011,0.0001073934,0.001300993,0.0001478983,0.001906102,0.00002451969,0.0002354017,0.000004327479],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997836,0.0000146531,0.0000190028,0.0000038928,2.466189e-7,0.000001126251,0.00002958013,0.00000211683,0.0001457822],"genre_scores_gemma":[0.9994943,0.00003010031,0.00007706004,0.000005410221,8.410429e-7,0.000002413093,0.00006847256,8.192193e-7,0.000320571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03559071,"threshold_uncertainty_score":0.0707671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004877644332422091,"score_gpt":0.2226889904580715,"score_spread":0.2178113461256494,"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."}}