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Record W2086907936 · doi:10.2166/aqua.2011.019

Water residence time in a distribution system and its impact on disinfectant residuals and trihalomethanes

2011· article· en· W2086907936 on OpenAlexaff
Andréanne Simard, Geneviève Pelletier, Manuel J. Rodríguez

Bibliographic record

VenueJournal of Water Supply Research and Technology—AQUA · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTrihalomethaneChlorineResidence time (fluid dynamics)Water qualityEnvironmental scienceHydrology (agriculture)ResidenceEnvironmental engineeringResidualWater treatmentDisinfectantTRACEREnvironmental chemistryChemistryEcologyGeologyMathematics

Abstract

fetched live from OpenAlex

The most critical water quality conditions are generally found at a water distribution network's extremities, given their high residence times. As chlorine injected at the water treatment plant (WTP) or at rechlorination sites has more time to react, residual chlorine concentrations at the extremities may not be enough to prevent microbial regrowth. This study focuses on the relationships between residence times, and residual chlorine and trihalomethane (THM) concentrations in a sector supplied with water coming directly from the WTP and from a large reservoir within the network. A hydraulic model was calibrated based on residence times obtained from a tracer study and a water quality characterization campaign at 47 sampling sites. Results showed that chlorine decay in water from the reservoir is faster than for water directly from the WTP. THM concentrations differ, with those in water from the reservoir being far higher than those from the WTP. A slight increase in THM concentrations is seen with residence time in both cases. By using a hydraulic model, we can evaluateat any time and in any placethe impact of a hydraulic change on the network in terms of hydraulics, as well as in terms of the vulnerability associated with low residual chlorine and high THM concentrations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.285
Teacher spread0.263 · 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 teacher head, 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

Citations21
Published2011
Admission routes1
Has abstractyes

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