11. Mitigation of Wildlife Collision Mortality at Little Cataraqui Creek Wetland
Bibliographic record
Abstract
Our research team is concerned with potential reptile and amphibian road mortality through Little Cataraqui Creek wetland along Front Rd. and Bath Rd. in Kingston, Ontario. The coastal marsh is a provincially significant wetland that supports species of migrating and breeding waterfowl, wetland dependent reptiles and amphibians, and may support some of Ontario’s threatened turtle species. The coastal wetland also provides a protected nesting area for a migrating species within Lake Ontario. Specific species of turtles, salamanders and frogs will be investigated. Our team will identify general breeding, nesting and migratory behavior patterns of associated wetland species, and establish potential indirect effects on the wetland ecosystem due to population decline. Traffic volume and speed limits of Front Rd. and Bath Rd. at Little Cataraqui Creek Wetland will be identified, and any current wildlife crossing systems in place will be evaluated. Based on this research, our team will establish the significance of wildlife road mortality within Little Cataraqui Creek Wetland at Front Rd. and Bath Rd., and propose suitable mitigation techniques. These techniques may include implementing structural design solutions such as road barriers, culverts, etc., as well as increasing public awareness of wildlife crossing on Bath Rd. and Front Rd. by proposing appropriate traffic laws in the area. Themes include wetland species breeding and nesting behavior, and urban/traffic development issues surrounding wetland ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".