Community-Driven and Reliability-Based Budget Allocation for Water Networks
Bibliographic record
Abstract
City managers and elected officials in many municipalities are frequently faced with the challenge to cater for public expectations on one hand and to comply with technical/engineering requirements on the other. This paper presents a four phased level of service driven reliability based methodology for allocation of budget to water mains. This methodology comprises of: (1) development of an Analytical Hierarchy model of LoS; (2) a sub-network criticality model to account for certain quantitative and qualitative characteristics of a given sub-network (3) a sub-network reliability assessment model and 4) a budget allocation model. Combination of the above contradicting requirements, i.e. level of service and network reliability, is expected to assist decision makers in quantifying the required condition improvement to meet service goals and to make more informed decisions on interventions and relayed priorities. To show the robustness of the developed models, a hypothetical case study is developed and analyzed.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".