The Use of Geographic Information Systems in the Development of a User-Pay Stormwater Utility in the Mimico Creek Watershed
Bibliographic record
Abstract
User fee systems are becoming increasingly popular at local levels of government. By shifting the burden from a tax and spend, to user-pay delivery of services, local governments are able to provide and manage local services with greater efficiency and accountability. A stormwater utility concept has been created for dealing with the often-expensive construction, maintenance, upgrading, and management of storm sewers and associated infrastructure. By examining the various user-pay systems for stormwater management, local governments and researchers can make a more informed decision on whether or not it is an appropriate method to raise revenues. The collection of fees is not based on consumption, as in many other public utilities, but on the property owner's contribution to the problem. Therefore, any user-pay stormwater utility must be easily understood and defensible to the general public. As well, the utility creation, administration, and management process can be aided by the use of a Geographic Information System (GIS). Data can be easily collected, stored, and analyzed, as well as be displayed in a way that is easy to understand, not only by the managers and analysts, but by the general public as well.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".