Optimizing source water blends for corrosion and residual control in distribution systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Utilities must understand the issues involved when multiple source waters are blended, particularly the effect on distribution system water quality. This article describes a multiobjective technique that can help evaluate blends to identify acceptable water quality for simultaneous control of lead, copper, iron, and monochloramine levels in distribution systems. Blends of three source waters—groundwater, surface water, and desalinated water—were evaluated. Modeling results indicated that different pipe materials often have conflicting water quality requirements for release abatement. For example, corrosion of copper and lead pipes was increased by increasing alkalinity, whereas increasing alkalinity was beneficial in reducing the release of iron corrosion products from pipes. Increasing sulfates reduced lead release but increased iron release. These conflicting water quality requirements for lead, copper, and iron release mean that utilities must evaluate the tradeoffs between water quality and corrosion response.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it