The effect of zinc orthophosphate and pH-alkalinity adjustment on metal levels leached into drinking water
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".