Sentinel lead pipe racks quantify orthophosphate’s dose-response in drinking water
Bibliographic record
Abstract
Orthophosphate is used to minimize lead contamination of tap water, but its benefits are difficult to quantify since lead concentrations are plumbing-dependent. Homes serviced by lead pipe are ideal for monitoring orthophosphate treatment, but best practices dictate the removal of lead once identified, which complicates sampling plans. Here we explore an alternative: recovered lead pipe racks supplied with distributed drinking water at various locations within a water system. We also propose a strategy for analyzing the data based on the generalized additive model, which approximates time series as a sum of smooth functions. In this study, geometric mean lead release from pipe racks exhibited a pronounced dose-response, falling by 54% after an increase from 1 to 2 mg PO4 L-1, and then climbing by 55% after a decrease to 1.5 mg PO4 L-1. Data from nine sentinel homes were consistent with those from pipe racks: geometric mean lead at the high orthophosphate dose was 60% of that at the low dose. Our results demonstrate sentinel pipe racks as a viable alternative to at-the-tap sampling for non-regulatory corrosion control monitoring. They also provide a Bayesian framework for quantifying orthophosphate’s effect on lead release that can incorporate information from multiple sources.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".