Impact of Major Highways on the Wildlife Population in Kingston and Frontenac County
Bibliographic record
Abstract
Over the past five years, there has been an abundance of interest concerning the ecological effects of major Ontario highways on the habitats and ecosystems of many wildlife populations. The primary concern with multilane, high-traffic freeways is that they typically divide existing habitats into relatively isolated zones. Consequently, this separates individuals within a population from other members of the same population, and also excludes access to many natural resources. The majority of the resultant issues for wildlife fall under three main categories; the collision based mortalities of organisms and the consequences on local residents, the halting of gene flow amongst the wildlife populations, and the physical intrusion and/or noise pollution adversely affecting the quality of habitat for local species. Based on these concerning issues, it is crucial for a sustainable solution to be developed and implemented in appropriate areas within Kingston and the surrounding Frontenac County. Our approach involves an extensive literature review, which will assist us in observing similar problems around the globe, as well as various solutions that have been executed to fix these said problems. Furthermore, we will conduct a thorough investigation of local organizations’ existing studies to obtain relevant data and statistics which will assist us in determining the effects high-traffic freeways have on the surrounding ecological environment. It is through this research that we hope to present valid findings on the multilane highways impact to local ecosystems and landscapes, as well as produce possible planning options for intervention and suggest key areas for further examination.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".