Bibliographic record
Abstract
Low Impact Development is intended to maintain pre-development hydrology, but this remains widely unproven due to a lack of large-scale implemented and monitored projects. The Creek Side Village (CSV) is a planned community that will convert a 28 ha fallow field into a mixed-density residential neighbourhood for seniors. The proposed neighbourhood will not include conventional stormwater management systems and will instead manage stormwater exclusively through Low Impact Development (LID). This thesis proposes an approach for evaluating the pre-development hydrologic conditions of a land development site and examines how hydrologic processes may change when developed with LID infrastructure for stormwater management. The property's pre-development hydrological conditions were characterized by collecting field data in the summer and fall seasons with comprehensive onsite measuring tools to understand the current seasonal hydrological condition. The development property has highly permeable soils with an average saturated hydraulic conductivity of 24 mm/hr, making the site well suited for infiltration-based LIDs. Evapotranspiration (ET) and seepage water data collected from high-resolution weighing lysimeters were analyzed to determine the site’s water balance. A comprehensive filtering process was developed and adopted to minimize the errors in the lysimeter data, thereby improving the estimate of ET. The water balance between the proposed LID neighbourhood was compared with data from an instrumented bioretention cell site to assess if this LID approach is likely to mimic the pre-development water balance. Results showed that green infrastructure could mimic the water balance of the fallow field and compensate for potential ET losses after development. Field data were used as input data for a pre-development hydrologic model (using SWMM and SWMM-UrbanEVA). The model was calibrated using the lysimeter water balance data. Hydrologic modelling was conducted to determine if specific LID approaches (permeable pavements and/or bioretention cells) can maintain pre-development water balance conditions after development. Four proposed post-development scenarios were considered. The modelling work reveals that bioretention cells are unlikely to maintain the pre-development water balance. Using PPs to control stormwater is a more effective approach, while PPs+BioCells can help maintain pre-development water balance, but they are less effective than PPs only.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".