GISRed 1.0, a GIS-based Tool for Water Distribution Models for Master Plans
Bibliographic record
Abstract
GISRed is a customized extension to ArcView® GIS3.2, oriented to modeling and calibration of water distribution networks, and which integrates all capabilities of the widely-used hydraulic modeling software EPANET 2.0.It basically includes a large set of hydraulic modeling tools, connections to the EPANET solver, and a hydraulic calibration module based on genetic algorithms.Additionally, more advanced modules to carry out complex tasks such as topological analysis, demand allocation and elevation interpolation tools, have been developed to enhance the features offered by the EPANET 2.0 interface and the inner capabilities of the supporting GIS platform.One of the most useful issues GISRed can help with, is in master planning.GISRed can simultaneously manage topological and structured data concerning the network elements and its properties, shape files containing auxiliary information and background layers.Information from the shape files and background can be imported to become part of the network; and, on the other hand, network information such as data or results can be queried to create new layers which can be symbolized to produce meaningful maps.In addition, results can be post-treated to create new information by using the spatial and geo-processing tools of the GIS platform.Finally GISRed can manage diverse scenarios in the same session to allow contrasting different results.This chapter focuses on the general procedure to be followed in order to develop a master plan based upon the authors' experience using the GISRed Extension.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".