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Record W2312295591 · doi:10.5942/jawwa.2016.108.0016

PEX and PP Water Pipes: Assimilable Carbon, Chemicals, and Odors

2015· article· en· W2312295591 on OpenAlexaff
Matthew Connell, Alexandra C. Stenson, Lauren Weinrich, Mark W. LeChevallier, Shelby L. Boyd, Raaj R. Ghosal, Rajarshi Dey, Andrew J. Whelton

Bibliographic record

VenueAmerican Water Works Association · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsAmerican Water (Canada)
FundersUniversity of South AlabamaNational Science Foundation
KeywordsOdorPolyvinyl chlorideTotal organic carbonPolyethylenePolypropyleneChemistryTolueneContaminationWater pipeWater qualityEnvironmental chemistryToxicologyPulp and paper industryEnvironmental scienceOrganic chemistryBiologyEcology

Abstract

fetched live from OpenAlex

Eleven brands of plastic drinking water pipe were evaluated for assimilable organic carbon (AOC) release at 23°C for 28 days: polyvinyl chloride, high‐density polyethylene, polypropylene (PP), and cross‐linked polyethylene (PEX) pipes. Three of eight PEX pipe brands exceeded a 100 µg/L AOC microbial regrowth threshold for the first exposure period, and no brands exceeded this value on day 28. No AOC increase was found for PP or PEX‐a1 pipes; the remaining pipe brands contributed marginal AOC levels. Pipe water quality impacts were more fully evaluated for two PEX‐b brands and one PP brand. PEX pipes caused greater odor than the PP pipe and released more organic carbon as well as volatile and semi‐volatile organic compounds. Water quality impacts were less after 30 days. Regulated and unregulated contaminants were found in three PEX plumbing systems. Drinking water odors were attributed to toluene, ethyl‐tert‐butyl ether, and unidentified contaminants.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations35
Published2015
Admission routes1
Has abstractyes

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