Development of a Low Impact Development and Urban Water Balance Modeling Tool
Bibliographic record
Abstract
The Toronto and Region Conservation Authority (TRCA), Lake Simcoe Region Conservation Authority (LSRCA), and Credit Valley Conservation Authority (CVC), “the GTA CAs,” have worked collaboratively to identify preferred low impact development (LID) stormwater management (SWM) models suitable for meeting typical design criteria. The GTA CAs research and experimentation efforts with various LID SWM case studies will inform the audience of model criteria and rationale that supports the selection of preferred open-source and/or commercial license LID modeling tool(s) which effectively demonstrate design targets have been met. The GTA CAs commissioned Golder Associates Ltd. to provide programming support and water resources expertise to support the development of the LID Treatment Train Tool (LID TTT) for Ontario, presently in a working beta version for review and experimentation before a planned hard launch in late March, 2017. The primary intention of the LID TTT is to support more consideration and realization of LID opportunities for a proposed site plan, throughout the design process undertaken by planners, SWM designers, and other decision makers for all types of site development or retrofit projects. The LID TTT will process computational results from EPA-SWMM to provide an assessment of stormwater runoff volume and associated target depths, along with total suspended solids (TSS) and total phosphorus (TP) reductions for both annual and event based scenarios, throughout the design process. A preliminary water budget assessment for pre and post conditions is also provided in the beta version of the LID TTT.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".