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By-Products Associated with Structural Rehabilitation for Water Distribution Systems

2009· article· en· W1967074450 on OpenAlexaff
Denis Gauvin, Benoît Lévesque, Jean-François Duchesne, Alain LeBlanc, éric Gaudreau, Pierre Ayotte

Bibliographic record

VenueEpidemiology · 2009
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsTap waterTriethylenetetramineChemistryDetection limitEnvironmental chemistryChromatographyEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

ISEE-0802 Background and Objectives: The rehabilitation of water distribution systems can be effected using a membrane impregnated with a thermosetting polymer. The objective of this study was to assess whether this process releases by-products in drinking water, especially bisphenol A diglycidyl ether (BADGE) and triethylenetetramine (TETA). Methods: Water samples were collected at two sites on the network. At each site, 2 samples were obtained before the rehabilitation work, and 8 additional samples were collected over a period of 48 hours after water circulation was restored. Samples were collected from the tap in glass bottles after a purge of 5 minutes. The samples were stored at −20° C and analyzed using liquid chromatography/time-of-flight mass spectrometry. Results: Concentrations of BADGE and TETA in samples collected before rehabilitation were all below the limit of detection (LOD: 0.05 and 2 μg/L respectively). Samples collected after water circulation was restored contained concentrations of BADGE ranging from 0.36 to 1.3 μg/L. Additional samples collected 1 year later still contained detectable concentrations (0.29 and 0.32 μg/L). Concentrations of TETA were below the LOD in all samples. Conclusion: The rehabilitation of the drinking water network with polymers may lead to the release of by-products in trace concentrations over a long period of time. Further analyses are needed to better understand the factors affecting their presence in drinking water.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.013
GPT teacher head0.228
Teacher spread0.216 · 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 designSimulation or modeling
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

Citations0
Published2009
Admission routes1
Has abstractyes

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