Understanding the Role of Particulate Iron in Lead Release to Drinking Water
Bibliographic record
Abstract
Lead service lines (LSLs) are a major source of drinking water lead, and high iron levels are frequently observed along with elevated lead release. A model distribution system, dosed with orthophosphate, was used to evaluate the effect of corroded iron distribution mains on lead release from recovered LSLs. Lead release was higher by 96 μg L(-1), on average, from LSLs supplied by corroded iron compared to the inert reference material (PVC). This effect may be explained by deposition of semiconducting iron oxide particles within LSLs. When galvanic cells with lead and magnetite (Fe3O4) electrodes were short-circuited, lead release increased 8-fold and a current averaging 26 μA was observed. In effluent from LSLs with an upstream iron main, colloidal lead and iron occurred in the same size fraction-possibly due to release of colloidal particles from LSL corrosion scale enriched with iron. Under these circumstances, high molecular weight (>669 kDa) (208)Pb and (56)Fe elution profiles, observed via size-exclusion chromatography, were highly correlated (average R(2) = 0.97). Increasing orthophosphate from 0.5 to 1.0 mg L(-1) (as PO4(3-)) accompanied an average reduction in lead release of 6 μg L(-1) month(-1) but did not significantly reduce the effect of an upstream iron main.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".