{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002361799,0.0001247437,0.0001518393,0.0001065608,0.0002524577,0.000009417152,0.0004904657,0.0000906714,0.001959612],"category_scores_gemma":[0.0001037662,0.0001197384,0.00003609559,0.0007149967,0.003564528,0.0003211797,0.0007409052,0.00009988697,0.001086082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004186834,"about_ca_system_score_gemma":0.000007147017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004811698,"about_ca_topic_score_gemma":0.00003379265,"domain_scores_codex":[0.998571,0.00002076801,0.0002385465,0.0004568194,0.0004227525,0.0002901084],"domain_scores_gemma":[0.9994222,0.00001444553,0.0002082348,0.0002538736,0.000001877471,0.00009935555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001380689,0.00001520584,0.4560072,9.091083e-7,0.000001836906,0.000001276602,0.0001085511,0.0001088874,0.5416954,0.00007613163,0.00002403421,0.001946812],"study_design_scores_gemma":[0.0002077884,0.000142067,0.2998894,0.000001839118,0.000011141,0.0000171922,0.0002099664,0.0004224143,0.6946995,0.0003034367,0.003981068,0.000114202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976773,0.0000134477,0.0002323953,0.0005786148,0.0001636047,0.0001888481,0.00001887178,0.00009543589,0.001031491],"genre_scores_gemma":[0.9993265,0.00003288728,0.000481851,0.00007595996,0.00001920789,0.00001322229,0.000003416905,0.000008870498,0.00003802728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1561178,"threshold_uncertainty_score":0.9996917,"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."}}