MétaCan
Menu
Back to cohort

Effect of High Chloride Concentrations on Microbial Regrowth in Drinking Water Distribution Systems

2015· article· en· W1624124780 on OpenAlexafffund
Masoumeh Sharafimasooleh, Jennie L. Rand, Margaret E. Walsh

Bibliographic record

VenueJournal of Environmental Engineering · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsDalhousie University
FundersAcadia University
KeywordsChlorineChlorideChemistryEnvironmental chemistryWater treatmentEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

This study was designed to identify the effects of variable concentrations of chloride on microbial regrowth, disinfection efficacy, and metal release in drinking water distribution systems. Annular reactors (ARs) containing cast iron or polycarbonate coupons were operated in this study to simulate distribution systems. Experiments were conducted with test water containing chloride concentrations of 10, 75, and 250 mg/L. Chlorine disinfection was applied to achieve 0.2 and 1.0 mg/L free chlorine residuals. Results showed that the ARs operated with elevated chloride concentrations were not significantly different from the ARs operated with low chloride concentrations in terms of microbial regrowth. However, there was a significant increase in iron concentrations after chlorination in the high chloride ARs. Further, more iron release was observed in these ARs at 1.0 mg/L compared to 0.2 mg/L chlorine residual. Higher chlorine doses were required to achieve goal residuals for those ARs with higher chloride concentration in cast iron systems. However, in polycarbonate systems, similar doses of chlorine were required to achieve the target free chlorine residual in both low-chloride and high-chloride ARs.

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)0.000
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 score0.586
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.171
Teacher spread0.167 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Environmental EngineeringSame topicWater Treatment and DisinfectionFrench-language works237,207