Wildlife Crossing Infrastructure for a Green Recovery: Emerging Opportunities for Innovation in Post-COVID-19 Recovery Efforts
Bibliographic record
Abstract
While roads are an essential part of modern life, they fragment habitats and landscapes. The effectiveness of wildlife crossing infrastructure (WCI) in reducing wildlife-vehicle collisions and reconnecting landscapes across roads are well documented in scientific literature, along with many other co-benefits. However, WCI projects are not implemented on a national scale in the US or Canada, in part due to lack of funding prioritization. This study undertook a thematic review of the US and Canadian infrastructure and spending plans to identify emerging opportunities for landscape connectivity and green infrastructure projects. The potential for co-locating WCI with active transportation uses was then explored in greater detail through an integrative literature review. WCI projects can contribute to national goals of climate resilience, economic recovery, and closing the infrastructure gap. However, positioning projects for funding will require strategic communication of the co-benefits of connected landscapes that align with national funding goals.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".