Loss in Carrying Capacity of Water Mains due to Encrustation and Biofouling, and Application to Walkerton, Ontario
Bibliographic record
Abstract
Encrustation and biofoulin.gcauses loss in carrying capacity of water mains and impacts water quality.Encrustation is a build-up of a slimy orange.browndeposit due to precipitation of calcium, iron and magnesium carbonates.Biofouling is the undesirable accumulation of a microbiological deposits in a biofilm layer.Biofilm accumulation is the result of physical, chemical, and biological processes, which play a major part in the microbial characterization of drinking water quality in distnoution networks.In this work, the decrease in pipe diameter and increased pipe roughness are computed as a function of service age of water mains.The method is then applied to the water distribution network in Walkerton, Ontario and the effects of encrustation and biofilm build-up with age and their effects on the hydraulics are investigated.Hydraulic analysis was performed by simulating the flow through the water supply network, assumed to be 100 years old, using EP ANET2.The results indicate that changes in the hydraulic parameters of a network can cause:1. a change in a flow pattern from reservoirs, 2. water to take a longer time to reach the far end of network, and 3. increased energy cost.It is also concluded that the available empirical model to compute pipe roughness and decrease in pipe diameter after a certain service time cannot be standardized, because of its dependence on the local and transient quality of water from various supply sources.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".