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Record W2331041159 · doi:10.2166/ws.2013.041

Haloacetic acid degradation by a biofilm in a simulated drinking water distribution system

2013· article· en· W2331041159 on OpenAlexafffund
C. Pluchon, Jean Sérodes, C. Berthiaume, Steve J. Charette, Geneviève Filion, Jade Fournier-Larente, Manuel J. Rodríguez, Caroline Duchaine

Bibliographic record

VenueWater Science & Technology Water Supply · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaBundesinstitut für SportwissenschaftUniversité Laval
KeywordsHaloacetic acidsDegradation (telecommunications)BiofilmChemistryEnvironmental chemistryChlorineOrganic matterTrichloroacetic acidBiomass (ecology)Natural organic matterContaminationChromatographyBacteriaOrganic chemistryEcologyBiology

Abstract

fetched live from OpenAlex

Haloacetic acids (HAAs) are disinfection by-products formed as a result of the reaction between chlorine and natural organic matter found in water. HAA concentrations have been observed to decrease at distribution system extremities. This decrease is associated with microbiological degradation by pipe wall biofilm. The objective of this study was to evaluate HAA degradation in a drinking water system in the presence of a biofilm and to identify the factors that influence this degradation. Degradation of dichloracetic acid (DCAA) and trichloroacetic acid (TCAA) was observed in a simulated distribution system. The results obtained showed that different parameters came into play simultaneously in the degradation of HAAs, including retention time, water temperature, biomass, composition of organic matter, and pipe diameter. Seasonal variations had a major effect on HAA degradation and biomass quantity was lower by 1 to 2 logs in the winter and spring compared with the fall. HAA removal decreased with increasingly large pipe diameters. The specific effects of each of these factors were difficult to isolate from each other owing to interactions.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.181
Teacher spread0.178 · 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

Citations24
Published2013
Admission routes2
Has abstractyes

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