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Record W2142300574 · doi:10.1139/l99-047

The effect of zinc orthophosphate and pH-alkalinity adjustment on metal levels leached into drinking water

2000· article· en· W2142300574 on OpenAlexvenueaboutno aff
Diane M. Churchill, D. S. Mavinic, Douglas G. Neden, Doug MacQuarrie

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2000
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsAlkalinityZincCopperLeaching (pedology)BrassMetallurgyTinCorrosionMetalSoft waterChemistryMaterials scienceEnvironmental scienceSoil water

Abstract

fetched live from OpenAlex

A 12-month pilot plant study was conducted to evaluate a number of corrosion control treatments to reduce metal leaching from typical household plumbing materials. A pipe loop system was used to test six treatment options, with a control loop of the characteristically soft, acidic, Greater Vancouver Water District (GVWD) water. Four pH- alkalinity combinations and two concentrations of zinc orthophosphate (0.37 mg/L and 1.0 mg/L as Zn) were studied. at regular intervals, the flowing, treated, and control waters were allowed to stagnate in the pipe loop system for 8- and 16-h periods. Following the designated standing time, water samples taken from the lead/tin solder jointed, soft copper plumbing coils, the submerged, free standing coils of 50/50 lead/tin solder, and the brass faucets, were measured for lead, copper, and zinc levels. Overall, when compared to the control loop, the pH-alkalinity treatments appeared to exacerbate metal leaching in standing samples. The zinc orthophosphate treated loops were both effective at reducing lead and copper leaching to below control levels. In general, the two standing times showed little difference in the levels of leached metals. The exception to this occurred in the higher dose zinc orthophosphate loop, which had higher copper and zinc levels at the longer standing time. Key words: copper, corrosion, drinking water, lead, zinc orthophosphate.

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)0.001
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 score0.019
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations21
Published2000
Admission routes2
Has abstractyes

Explore more

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