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Record W4381798696 · doi:10.3390/w15132325

Drinking and Natural Mineral Water: Treatment and Quality–Safety Assurance

2023· article· en· W4381798696 on OpenAlexafffund
Tryfon Kekes, Constantina Tzia, Γεώργιος Κολλιόπουλος

Bibliographic record

VenueWater · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScope (computer science)SanitationLegislationWater qualityWater treatmentHuman lifeHazardEnvironmental planningEnvironmental scienceQuality assuranceMineral waterBusinessWaste managementRisk analysis (engineering)Water resource managementEnvironmental engineeringEngineeringOperations managementComputer scienceGeography

Abstract

fetched live from OpenAlex

Water is one of the most essential materials for human survival and preservation of life. The significance of equitable access to safe and clean drinking water and sanitation is established as a human right that is essential for the full enjoyment of life. The scope of this review is to record the different water sources, along with the water’s main characteristics of each source, and to compare the existing regulations and legislations regarding drinking water as have been set by the competent organizations. Furthermore, this review will highlight and provide a general insight into the most applied drinking water treatment methods and introduce some novel treatment methods. Moreover, Hazard Analysis and Critical Control Points (HACCP) requirements and case studies of drinking water treatment plants are presented. Finally, natural mineral water characteristics, treatment methods, and legislation, along with the uses and treatment of water in the food industry, are also discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.261
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2023
Admission routes2
Has abstractyes

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