{"id":"W4381798696","doi":"10.3390/w15132325","title":"Drinking and Natural Mineral Water: Treatment and Quality–Safety Assurance","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scope (computer science); Sanitation; Legislation; Water quality; Water treatment; Human life; Hazard; Environmental planning; Environmental science; Quality assurance; Mineral water; Business; Waste management; Risk analysis (engineering); Water resource management; Environmental engineering; Engineering; Operations management; Computer science; Geography","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":[0.001126523,0.0005209121,0.0005981039,0.001243702,0.0005137782,0.00157005,0.0006456812,0.001410866,0.00252156],"category_scores_gemma":[0.0008981579,0.0002464626,0.0005991725,0.001313584,0.001048915,0.001485002,0.0008577903,0.001061991,0.001149997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260271,"about_ca_system_score_gemma":0.002270425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002646517,"about_ca_topic_score_gemma":0.002458876,"domain_scores_codex":[0.9985383,0.0002275259,0.0001299225,0.000178272,0.000806264,0.0001198529],"domain_scores_gemma":[0.9995297,0.0001465515,0.0001176771,0.00002508343,0.0001597733,0.00002120016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000168602,0.0002132947,0.002274652,0.02576884,0.0001505268,0.000814502,0.0004880671,0.003651281,0.2234107,0.05727383,0.01337651,0.6724092],"study_design_scores_gemma":[0.00001447739,0.0003774351,0.002945312,0.001560372,0.00009284417,0.001541949,0.0004441801,0.00160486,0.1820126,0.01148268,0.7978733,0.00005005988],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.06822304,0.7080994,0.1350838,0.01151376,0.001800548,0.0006589748,0.001125759,0.0004388744,0.07305589],"genre_scores_gemma":[0.3018846,0.5977804,0.06316464,0.002786776,0.001144863,0.0003789473,0.001018716,0.0001452769,0.03169576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002646517,"threshold_uncertainty_score":0.009143889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195643208327422,"score_gpt":0.2612153853139499,"score_spread":0.2416510644812077,"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."}}