Assessing downstream geomorphic implications of low-impact development (LID) projects in the Spencer Creek watershed
Bibliographic record
Abstract
The Spencer Creek watershed, located in Hamilton, Ontario, plays a complex role in connecting an extensive network of rivers to the western end of Lake Ontario. Over the past decade, the watershed has seen an increase in urban activity, contributing to notable transformations in the natural landscape. Changes in land use have impacted not only zoning patterns in the region but also the overall quality of surrounding fluvial environments. With climate change and urbanization rates advancing, the city of Hamilton has taken measures to implement advanced stormwater management strategies, including low-impact development (LID). The purpose of these techniques is to imitate the behaviour of natural water systems and assist in stormwater management efforts within cities, often by reducing impervious surfaces. This study used stream power-based analyses to assess the effects of land-use change and LIDs on river networks. Stream power, representing energy expenditure per unit time, is a well-established metric for evaluating geomorphic sensitivity. Sample sites were surveyed for bankfull width measurements in summer 2022 and winter 2024. The data was used to derive empirical values specific to the Spencer Creek watershed and applied to the Stream Power Index for Networks (SPIN) tool. The SPIN tool was then used to generate various scenarios to investigate the post-implementation impacts of LIDs. A range of existing LIDs in Hamilton and theoretical land use changes based on the literature were simulated. Overall, the results of this study offer insight for improving river management strategies and addressing water resource concerns amid evolving climate conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".