Urban Beaver Activity Management and Qualitative Risk Assessment Using GIS
Bibliographic record
Abstract
Extreme weather events are expected to increase the burden on urban stormwater infrastructure as the climate continues to change. Beavers (castor canadensis) are well known for their engineering prowess and ability to rapidly naturalize degraded urban streams. This has a multitude of benefits including stormwater retention, groundwater recharge, ecological restoration, and erosion reductions; however, beavers and stormwater managers frequently clash through the clogging of stormwater outlets and the destruction of beaver dams. Beavers are routinely trapped, and their dams destroyed, only to have new beavers re-colonize the area in an endless, costly cycle. This project demonstrates a GIS-based method to predict where beaver activity may be an issue in a stormwater network using land cover to identify ideal beaver habitat at chokepoints in a stormwater network. Areas where roads cross streams in the Fletcher’s Creek watershed in Brampton, Ontario were ranked based on the amount of land cover nearby that could increase the likelihood of beaver habitation and associated blocking of sensitive stormwater infrastructure such as culverts at stream-street crossings. This ranking could theoretically allow stormwater managers to prioritize monitoring. If this method is tested and verified in the field it could prove useful for stormwater infrastructure planners by allowing them to invest in cost-effective beaver deterrents at key locations before a human-beaver conflict arises, potentially reducing costs for maintenance activities and allowing beavers to continue provide their natural stormwater management services.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".