RE-INVENTING URBAN HYDROLOGY IN BRITISH COLUMBIA: RUNOFF VOLUME MANAGEMENT FOR WATERSHED PROTECTION
Bibliographic record
Abstract
There is a logical link between changes in hydrology and impacts on watershed health, whether those impacts are in the form of flooding or aquatic habitat degradation. The link is the volume of surface runoff that is created by human activities as the result of alteration of the natural landscape (i.e., through removal of soils, vegetation and trees). When trees, vegetation and soils are replaced by roads and buildings, less rainfall infiltrates into the ground or is taken up by vegetation, which results in more rainfall becoming surface runoff. The key to protecting urban watershed health is to maintain the water balance as close to the natural condition as is achievable and feasible by preserving and/or restoring soils, vegetation and trees. But accomplishing this requires major changes in the way we approach urban drainage and in the way we develop land. Drainage engineers have traditionally thought of reconciling pre- and post-development runoff in terms of flow rates, not volumes. At the site level, however, we need to focus on how much rainfall volume has fallen, how to capture the excess, and what to do with it. The Province of British Columbia in the Pacific Northwest is leading the way in North America in developing and implementing innovative criteria and methodologies for reducing excess runoff volumes at the source, where rain falls. Science-based performance objectives and targets have been established to mimic the hydrology of a natural
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".