Factors influencing transboundary wildlife management in the North American ‘Crown of the Continent’
Bibliographic record
Abstract
Jurisdictional boundaries and borders are rarely coincident with ecological systems. The long-term persistence of viable wildlife populations and habitats, especially for highly mobile and migratory species, is contingent upon effective management that transcends administrative boundaries. Although transboundary natural resource management has emerged as a topic of academic and professional discourse, implementation has been hampered by a host of barriers that include institutional, administrative, financial and contextual factors. The Crown Managers Partnership, a collaborative initiative of public land managers in the transboundary Rocky Mountains of Canada and the United States, is exploring the approaches to overcome these barriers. This paper reports on the results of interviews to identify the factors that influence the management of transboundary wildlife and provides a series of recommendations that are specific to the study area context, but are also transferable to other regions. Formalizing the existing partnership, exploring options for expanding participation in the partnership to include non-government interests, engaging third party facilitation, using non-traditional data sources, applying metapopulation ecology theory, and interdisciplinary problem solving are all elements recommended for improved transboundary management and of wildlife in the Crown of the Continent Ecosystem.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".