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Record W2333015911 · doi:10.1061/40507(282)61

Impact of Household Plumbing Materials on Trace Metal Levels in Drinking Water in Regina, Canada

2000· article· en· W2333015911 on OpenAlexaffabout
T. Viraraghavan, K. S. Subramanian, S. Tanjore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsOceanWorks International (Canada)University of ReginaHealth Canada
Fundersnot available
KeywordsWater qualityEnvironmental scienceLeaching (pedology)Sampling (signal processing)Heavy metalsEnvironmental engineeringTap waterTrace metalSignificant differenceEnvironmental chemistryMetalChemistryMetallurgyEngineeringSoil waterMaterials scienceMedicine

Abstract

fetched live from OpenAlex

A study was initiated in the City of Regina, Canada to investigate the effect of plumbing material on the drinking water quality. Three rounds of sampling were conducted during November, 1994 to January 1995. Over a hundred residents participated in this study for each of the sampling rounds. Approximately 600 violations of Canadian drinking water guidelines (CDWG) maximum allowable concentration (MAC) levels were observed during the study. Violation of the MAC levels was noted to be the maximum during the first round. The effect of the area of the city on the heavy metal concentration in the drinking water samples was significant. The sampling round or the month of sampling had a significant effect on the heavy metal concentration. The effect of the type of dwelling was not significant on the metal concentration found in the drinking water. Plumbing age and the material of plumbing had a significant effect on the leaching of the heavy metals in the drinking water samples in each of the three rounds.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.017
GPT teacher head0.217
Teacher spread0.199 · 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 designObservational
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
Published2000
Admission routes2
Has abstractyes

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