Evaluating the Effects of Full and Partial Lead Service Line Replacement on Lead Levels in Drinking Water
Bibliographic record
Abstract
Lead service line replacement (LSLR) is an important strategy for reducing lead exposure via drinking water, but jurisdictional issues can sometimes interfere with full replacement of the lead line. The effects of full and partial LSLR on lead levels were assessed using 5 × 1-L sample profiles collected at more than 100 single-unit residences. Profiles comprised four sequential standing samples (L1-L4) and a free-flowing sample (L5) drawn after a 5 min flush of the outlet. At 45 sites with full lead service lines, 90th percentile lead levels in standing samples ranged from 16.4 to 44.5 μg L(-1) (L1 and L4, respectively). In the free-flowing sample (L5), 90th percentile lead was 9.8 μg L(-1). Within 3 days, full LSLR had reduced L3-L5 lead levels by more than 50%, and within 1 month, lead levels were significantly lower in every liter of the sample profile. Conversely, partial LSLR more than doubled premises plumbing (L1, L2) lead release in the short term and did not reduce L1, L2 lead release in the long term. Even 6 months after partial LSLR, 27% of first-draw lead levels were greater than 15 μg L(-1) (the U.S. EPA action level), compared with 13% pre-replacement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".