Species and Areas Under Protection: Challenges and Opportunities for the Canadian Northern Corridor
Bibliographic record
Abstract
The Canadian Northern Corridor (CNC) is a proposed multimodal transportation right-of- way, with accompanying infrastructure, that would run largely through northern Canada, with the goal of connecting all three coasts. Given the magnitude of the project, there are many implications for the lands and waterways, as well as for humans and other species in those areas, that the CNC will either intersect directly or affect indirectly through cascading effects. This study used literature searches focused on the intersection of biodiversity, conservation research, government policies and engagement with Indigenous knowledge systems. Given the diversity of topics and the amount of research available in some areas (e.g., entire reviews have been written solely focused on the ecological effects of roads), this study highlights, rather than comprehensively treats, potential biodiversity challenges associated with the CNC. Biodiversity is a term that refers to the diversity (variability or complexity) of life, typically at one or more of the following levels: genes, species and ecosystem. Major development projects may: 1) reduce genetic diversity within species, 2) increase odds of species loss in the region, and 3) degrade the quality and extent of a variety of 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".