{"id":"W3023779228","doi":"10.1021/acs.est.9b07718","title":"Lakes at Risk of Chloride Contamination","year":2020,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Environment and Climate Change Canada","funders":"Division of Environmental Biology","keywords":"Environmental science; Watershed; Chloride; Hydrology (agriculture); Contamination; Ecology; Chemistry; Geology; 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":[0.0003876313,0.0002077188,0.0001990079,0.0003699462,0.0003672041,0.0006182737,0.000207089,0.0002331225,0.001592086],"category_scores_gemma":[0.00158277,0.0001891596,0.0004004269,0.0006154739,0.0001493108,0.0003464004,0.0005879301,0.0002314888,0.0001871234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006423809,"about_ca_system_score_gemma":0.000841343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06193891,"about_ca_topic_score_gemma":0.1060254,"domain_scores_codex":[0.999756,0.00005768655,0.00001573664,0.00007195232,0.00005065023,0.00004789174],"domain_scores_gemma":[0.999428,0.0001389476,0.0002380915,0.00004114941,0.0001223025,0.00003148116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005412525,0.00002073228,0.9742631,0.00001961944,0.0000699569,0.00012618,0.00008446464,0.008423755,0.0005730191,0.0001265723,0.0007471088,0.01549143],"study_design_scores_gemma":[0.00001998408,0.00006873674,0.9129323,0.00005065421,0.0001062718,0.0002381202,0.0006864975,0.08011229,0.001476291,0.001165489,0.003120968,0.00002247021],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935793,0.0001182457,0.002724052,0.0002054501,0.000004035879,0.00001558585,0.001869278,0.00009672797,0.001387385],"genre_scores_gemma":[0.9966463,0.00009169808,0.001629699,0.00003931598,0.000002901843,0.00001028723,0.001221096,0.00000702093,0.0003516619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06193891,"threshold_uncertainty_score":0.1231568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00442595874612756,"score_gpt":0.1827333120057762,"score_spread":0.1783073532596486,"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."}}