STORMWATER MANAGEMENT DESIGN: MODEL FOR A SENIOR UNDERGRADUATE COURSE
Bibliographic record
Abstract
Stormwater management design makes up half of a fourth year undergraduate course that also includes design of water distribution systems and wastewater and stormwater collection systems. Learning is supported by lectures with PowerPoint presentations that include many images. Early emphasis is placed on understanding the effects of urbanization, in terms of hydrology, geomorphology, water quality and stream ecology. This leads to the objectives of stormwater management and discussion of both traditional and emerging design criteria, as well as provincial guidelines and municipal standards. The students gain an appreciation of the need for a ‘treatment train’ approach – prevention followed by lot-level, conveyance, and if necessary, end-of-pipe measures – to achieve the multiple objectives of stormwater management. Students master screening-level design that addresses land use, physical feasibility, climate, watershed opportunities and constraints, community factors, and the capability for various stormwater management practices (SWMPs) to meet various water quantity and quality objectives. Details and design examples for a range of SWMPs such as infiltration trenches, biofiltration swales, and extended detention wet ponds are presented, and a field trip reinforces the in-class information. The students gain design experience by means of a term project in which four person teams design the water-related infrastructure of a new subdivision. A Terms of Reference is provided at the beginning of the term. The design teams are responsible for a proposal, which initiates early project management, an oral progress report, and a final design report and presentation. Design labs provide an opportunity to introduce students to the software (EPA SWMM 5.0) but are primarily devoted to working on the design project. The design project is evaluated in terms of product and process, and both group and individual marks are assigned. Designs are evaluated prior to the final presentations enabling the instructor to provide substantive feedback before the course concludes. For the most part, the quality of work done by the students over the last several years has been exceptional. During the course, common feedback from the students includes “lack of direction” and “heavy work load.” Following the course, students appreciate the understanding of the subject material and the design experience gained, which they find to be very relevant to employment opportunities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".