{"id":"W4399809398","doi":"10.1145/3653724.3653764","title":"Prediction of Water's Safety for Consumption by Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Water consumption; Computer science; Consumption (sociology); Machine learning; Environmental science; Water resource management","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":[],"consensus_categories":[],"category_scores_codex":[0.0003320728,0.00004931509,0.00006375988,0.00002407918,0.00007169682,0.000005390636,0.00009519079,0.00004861803,0.0002222647],"category_scores_gemma":[0.00004497,0.00003565534,0.00002166317,0.00006370374,0.00007843499,0.00007917889,0.0001354099,0.00005073369,0.0002525131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004779867,"about_ca_system_score_gemma":4.899218e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001594193,"about_ca_topic_score_gemma":0.000005547532,"domain_scores_codex":[0.9994668,0.00001924344,0.0001358077,0.0001276302,0.00011143,0.0001390464],"domain_scores_gemma":[0.9998179,0.00003915274,0.00002497858,0.0001007673,0.000003069471,0.00001413085],"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":[0.0000265851,0.00002142728,0.4347608,0.00002964241,0.000009293117,2.056125e-7,0.0002456514,0.001737453,0.5505568,0.0000792482,0.007351175,0.005181663],"study_design_scores_gemma":[0.0003013118,0.0001244257,0.04931451,0.000006979979,0.00000692777,6.13383e-7,0.00008430566,0.008968533,0.9071878,0.001039589,0.0328868,0.00007822669],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937099,0.000004369774,0.004565082,0.0004777811,0.0001040538,0.0001560504,0.00004179038,0.000678755,0.0002622456],"genre_scores_gemma":[0.9956837,0.00003559052,0.001120037,0.00000447625,0.000008128335,0.00001948994,0.0001101886,0.000006644244,0.00301176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3854463,"threshold_uncertainty_score":0.3245629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04922020987977347,"score_gpt":0.2608927665033856,"score_spread":0.2116725566236121,"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."}}