Effectiveness of Sodium Silicates for Lead Corrosion Control: A Critical Review of Current Data
Bibliographic record
Abstract
Orthophosphate is commonly used to control lead release to drinking water, but it is a potential source of nutrient pollution and can increase the concentration of particulate and colloidal lead. Given these drawbacks, there is considerable interest in alternative corrosion control treatments. While less common than orthophosphate, sodium silicate is recognized as a treatment for controlling lead release to drinking water. But there is no consensus in the scientific literature as to whether it is effective. Here, we conduct a data summary of the peer-reviewed literature pertaining to silicate-based corrosion control of lead. We find that silicate treatment generally accompanied higher lead release than the equivalent (pH-matched) system without sodium silicate (0.5–21.5 times higher). Moreover, silicate treatment was inferior to orthophosphate treatment; sodium silicate accompanied 1.0–65 times more lead release than the equivalent orthophosphate-treated system. Sodium silicate’s positive effect on pH, then, appears to be the main driver of lead release control. While it is possible that under some circumstances silicate treatment promotes formation of a solid phase that either limits equilibrium solubility or slows lead release, the mechanism has not been described precisely.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".