Optimisation of Corrosion Control for Lead in Drinking Water Using Computational Modelling Techniques
Bibliographic record
Abstract
In many respects, lead in drinking water has become a forgotten problem, since the mid 1980s when a range of environmental controls were implemented to reduce exposure to lead. This is largely because the sampling protocols, that underpin regulatory controls, are mostly inadequate and have tended to under-estimate the amount of lead that can be present in drinking water (IWA, 2010).Optimisation of Corrosion Control for Lead in Drinking Water Using Computational Modelling Techniques shows how compliance modelling has been used to very good effect in the optimisation of plumbosolvency control in the United Kingdom, particularly in the optimisation of orthophosphate dosing. Over 100 water supply systems have been modelled, involving 30% of the UK's water companies. This “proof-of-concept” project has the overall objective of demonstrating that these modelling techniques could also be applicable to the circumstances of Canada and the United States, via three case studies.This title belongs to Best Practice Guides on Metals and Related Substances in Drinking Water.ISBN: 9781780404790 (eBook)ISBN: 9781780404783 (Print)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".