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Record W2093410368 · doi:10.1139/l05-043

Assessing the impact of corrosion control measures on tap drinking water of the Greater Vancouver Regional District

2005· article· en· W2093410368 on OpenAlexfundvenueaboutno aff
Gillian Frances Knox, D. S. Mavinic, J. W. Atwater, Doug MacQuarrie

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTap waterZincCopperCorrosionWater qualityEnvironmental scienceWater sourceWater treatmentEnvironmental engineeringMetallurgyMaterials scienceWater resource management

Abstract

fetched live from OpenAlex

A study on the quality of water distributed within the Greater Vancouver Regional District (GVRD) was carried out to evaluate the effectiveness of previously implemented corrosion controls. In addition, the effect of temperature fluctuations was investigated to determine if it influenced the efficacy of the control measures. The GVRD was divided into four distribution areas. The Capilano water had no corrosion control treatment. Seymour, Coquitlam, and Newton waters were treated with soda ash, resulting in a pH of 6.8, 6.9, and 8.1, respectively. Standing cold water and running hot and cold water samples were collected from the four zones and analyzed for lead, copper, and zinc concentrations. The source water did not influence the amount of lead at the tap. The source water did influence the amount of copper and zinc at the tap. The highest copper and zinc concentrations were found in the water with the lowest pH (Capilano) and the lowest concentrations in the water with the highest pH (Newton). Seasonal temperature fluctuations of distributed GVRD water did not affect the metal concentrations in the water.Key words: corrosion control, GVRD drinking water, copper, lead, zinc, pH adjustment.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.990

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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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 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

Citations5
Published2005
Admission routes3
Has abstractyes

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