A Novel Lazy Serpent Algorithm for the Prioritization of Leak Repairs in Water Networks
Bibliographic record
Abstract
Maximizing benefit from budget allocation is a major challenge for municipalities in the modern era. This is especially significant when it comes to infrastructure network management such as water distribution networks. The main challenges of water distribution networks are leakage and leak repairs. Municipalities commonly use first-in-first-out approaches to determine which leaks to allocate budget for first. Yet, the deterioration of leaks is not linear through time and requires a more in-depth assessment of the condition of the leak. Therefore, this article presents two prioritization approaches for the scheduling of leaks while incorporating deterioration over time. This paper proposes and compares two optimization techniques: (1) a well-known genetic algorithm and (2) a novel approach named the Lazy Serpent Algorithm. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal order by using much less computation time. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits.
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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".