Evaluating transboundary connectivity to support cross-border conservation between Canada and the United States
Bibliographic record
Abstract
Abstract Landscape connectivity is considered critical for maintaining biodiversity. Many jurisdictions have identified the importance of considering connectivity in land use plans, and connectivity has recently been included as a metric in international conservation agreements. Consequently, there is a need for measures of connectivity that can be applied at national and international scales; however, evaluating connectivity across international boundaries remains a challenge due to inconsistencies in mapping data, and differences in sociopolitical systems. Canada and the United States of America (USA) share a long international border, and thus, there is a need and opportunity to develop transboundary connectivity plans for this international region. We extended a previously published pan-Canadian multi-species connectivity model into the USA to produce a seamless and high-resolution omnidirectional connectivity map for the two countries. We identify several significant animal movement corridors across the transboundary region and show that about 20% of connectivity hotspots in the region are covered by protected areas. We also demonstrate the potential of the map for identifying important areas for wildlife movement and the spread of zoonotic diseases. Our map will be useful for supporting transboundary connectivity conservation between Canada and the USA and our modelling approach can easily be applied to other countries to support their own connectivity initiatives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".