Regional curves to support stream restoration initiatives in Southern Ontario
Bibliographic record
Abstract
An important first step in channel restoration design is determining appropriate bankfull dimensions. However, bankfull indicators are often absent or unreliable in creeks needing restoration. To help solve this problem, this study developed a series of regional curves for selected hydrophysiographic regions of southern Ontario where the principal controls on channel form, such as surface geology and precipitation, are uniform. The curves relate bankfull dimensions at riffles to watershed drainage area. Riffles were measured since these features tend to have consistent cross-sectional areas whereas pool cross-sectional areas may vary greatly. This study focused on creeks in smaller watersheds (<100 km2) because restoration projects tend to be located in these areas. A total of 31 creeks were measured in three different hydrophysiographic regions of southern Ontario, with at least nine creeks measured in each region. Bankfull geometry equations, as related to drainage area, were developed for the regions of Waterloo, Peel and York-Durham. Bankfull cross-sectional area and drainage area were strongly related (R2 ≥ 0.95) in all three regions. The relation between drainage area and other bankfull dimensions, such as width and mean depth, was not as strong but was statistically significant. The results supported combining data from all regions into a single set of composite curves, and it is recommended that these composite curves be applied in practice. The composite regional curves may be used to provide an indication of appropriate bankfull dimensions for an impaired watercourse that lacks reliable bankfull features. The curves may be referenced by stream designers to assist with the design process, and by regulators who are tasked with reviewing channel designs. This paper describes how sites were selected, outlines the process used to collect and analyze data and provides examples of how the curves may be applied to design.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".