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Record W2489523217 · doi:10.1111/jam.13243

The effect of chlorination and hydrodynamic shear stress on the persistence of bacteriophages associated with drinking water biofilms

2016· article· en· W2489523217 on OpenAlexaff
Sandra Pelleïeux, Laurence Mathieu, J. C. Block, Christophe Gantzer, Isabelle Bertrand

Bibliographic record

VenueJournal of Applied Microbiology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsDouglas Mental Health University Institute
FundersAgence de l'Eau Seine-NormandieUniversité de Lorraine
KeywordsBiofilmPersistence (discontinuity)MicrobiologyShear stressShear (geology)Environmental scienceEnvironmental chemistryChemistryBacteriaBiologyGeotechnical engineeringMaterials scienceGeologyComposite material

Abstract

fetched live from OpenAlex

AIMS: This work aimed to assess at pilot scale the effect of chlorination and water flushing on 2-month-old drinking water biofilms and, above all, on biofilm-associated F-specific RNA bacteriophages MS2, GA and Qβ. METHODS AND RESULTS: Chlorination (4 mg l(-1) ) was applied first with a hydrodynamic shear stress of 1 Pa and second with an increase in hydrodynamic shear stress to 10 Pa. Despite a rapid decrease in the number of biofilm bacteria and associated phages, infectious phages were still detected on surfaces after completion of the 150 min cleaning procedure. The resulting sequence of phage removal was: GA > Qβ ≫ MS2. CONCLUSIONS: The effect of chlorine on biofilm bacteria and biofilm-associated phages was limited to the upper layers of the biofilm and was not enhanced by an increase in hydrodynamic shear stress. A smaller decrease was observed for MS2 than for GA or Qβ after completion of the cleaning procedure. SIGNIFICANCE AND IMPACT OF THE STUDY: The differences observed between the three phages suggest that the location of the viral particles in the biofilm, which is related to their surface properties, affects the efficiency of chlorine disinfection.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.099

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 head0.167
Teacher spread0.163 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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