Evolution of the relationship between total suspended solids concentration and turbidity during flushing sequences of water pipes
Bibliographic record
Abstract
Abstract Particle accumulation and circulation in water distribution systems are significant in the development of good management practices to protect against discoloration events, which are a major cause of water customer complaints. Quantifying the amount of particles deposited in water pipes is usually done by obtaining total suspended solid measurements while performing flushing sequences, which requires time, skills, and equipment. Some authors explored the possibility of rapidly approximating total suspended solids concentration (TSSC) in water pipes by measuring water turbidity on site, but they obtained different results and coefficients of correlation. This paper presents the results of tests performed in the laboratory on a test loop. Unidirectional flushing (UDF) and air scouring sequences were performed under various hydraulic conditions and two different particle origins. Samples were obtained along each sequence, and the turbidity and TSSC were measured. The results illustrate that the ratio between turbidity and TSSC may vary greatly between samples, up to 10 times during UDF sequences and 20 times during air scouring sequences. Particle origin, flushing method, and sampling time are all factors impacting the turbidity/TSSC ratio. This is why TSSC should not be estimated from a single turbidity reading.
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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 | 0.001 |
| 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) | 0.001 | 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".