Software Engineering Issues in the Design of an Upwardly-Complex Water Network Analysis Program
Bibliographic record
Abstract
Building a computer program for the steady state simulation of water distribution networks using state-of-the-art techniques and graphical user interfaces (GUI) involves the interaction of several disciplines and mobilization of appropriate resources.This chapter reports on the experience of building such software.The mathematical model is described and the use of graph theory tools in the description of networks together with an algorithm for the treatment of Pressure Reducing Valves have been highlighted.The GUI organization and CASE tools used are described.During testing of the program, lack of benchmark data has been recognized together with the major sources of uncertainties and it is imperative that benchmarks should be developed.The factors that influence quality assurance during the crucial phase of software development have also been identified and discussed in the light of the present experience.Although similar commercial packages do exist, this software will be used as a platform for future extensions to include: extended period simulation; explicit determination of network parameters; and the capability to simulate unsteady conditions.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".