{"id":"W4392462885","doi":"10.1016/j.scitotenv.2024.171485","title":"Geospatial analysis of groundwater arsenic and fluoride in Quaternary aquifers of southern Ontario, Canada","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Nuclear Laboratories; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Economic and Social Research Institute; University of Guelph","keywords":"Groundwater; Geology; Overburden; Aquifer; Water well; Hydrology (agriculture); Bedrock; Water table; Spatial distribution; Glacial period; Geomorphology; Mining engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002149595,0.0002056645,0.000284266,0.003368989,0.001922503,0.0009944154,0.0005295375,0.0002566859,0.00136057],"category_scores_gemma":[0.0008085939,0.0001992534,0.0003634806,0.006808254,0.0006416655,0.0002507587,0.0006170268,0.0001931395,0.0001479871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01688348,"about_ca_system_score_gemma":0.01683734,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9956422,"about_ca_topic_score_gemma":0.9981833,"domain_scores_codex":[0.9997124,0.00002004568,0.00002126915,0.00005086395,0.0001193223,0.00007612869],"domain_scores_gemma":[0.9993033,0.00004824817,0.0000874687,0.00002120333,0.0004775659,0.00006223149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001832843,0.00005417754,0.9609904,0.0001525774,0.0001342306,0.0003307912,0.002408956,0.007239097,0.003632253,0.001324197,0.003021595,0.02052842],"study_design_scores_gemma":[0.000006922364,0.000008010388,0.9901313,0.00002294963,0.00003174037,0.00003654636,0.002239176,0.00347609,0.0002637994,0.00008922571,0.003683048,0.00001105],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901789,0.000244087,0.0003090689,0.0001220769,0.000004590239,0.0000330946,0.005595815,0.00002963793,0.003482603],"genre_scores_gemma":[0.9948521,0.0002221273,0.0006530346,0.00001740914,0.000002330599,0.00001404561,0.002220877,0.000006536483,0.002011503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01688348,"threshold_uncertainty_score":0.1224988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004499136047453221,"score_gpt":0.1777830820168769,"score_spread":0.1732839459694237,"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."}}