Effects of Concrete Barriers on Wildlife Crossing in the West Kootenay Region, British Columbia
Bibliographic record
Abstract
I explored the effects that animal vehicle collisions have on vehicle operators as well as wildlife species through extensive literature based research. I also examined the relationship between roadways and wildlife, and specifically how roadways can isolate populations, fragment habitat, and fragment territories, as well as how structures along roadways impact animal crossing. I conducted a pilot study along the 42.6 kilometer stretch of Highway 3A from Castlegar to Nelson, British Columbia. The field work involved using ArcGIS’s Survey123 application to record roadkill data points over a four-month study period. The objective of this study were to determine the animal species most frequently stuck along this stretch of highway, identify roadkill hotspots, determine if there is an increase in the number of animals hit when there are concrete barriers present along the sides of this highway, and to determine an effective roadkill mitigation technique that could be implemented along Highway 3A. From the pilot study I determined there is no obvious relationship between the number of animals struck and concrete barriers being present along the side of the highway. From my study I found wild turkeys to be the most frequently hit species, followed by black bears and squirrels. I identified and mapped out two prominent roadkill hotspots along Highway 3A and determined through background research that the most effective roadkill mitigation technique would be an overpass at each roadkill hotspot with at least five kilometers of fencing on either side of the entrances to the overpasses. I concluded that the relationship between roadways and wildlife needs to be better understood and that similar future studies would be valuable to better understand these relationships. It would be valuable for the Ministry of Transportation and Infrastructure to invest more money into implementing more roadkill mitigation techniques such as crossing structures combined with fencing to prevent future vehicle-animal collisions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".