Long-term wildlife research and monitoring sites in Arctic Canada
Bibliographic record
Abstract
In Arctic Canada, the development of long-term research sites to study vertebrates (principally marine birds and marine mammals) has supported the safe delivery of collaborative science that is unique in its ability to assess the impacts of rapid environmental change and increasing human-generated pressures (e.g., fisheries bycatch, harvest, tourism, resource development) to wildlife and their habitats. The longevity of these research programs has enabled them to be impactful in their ability to inform environmental and wildlife conservation and management actions in Canada and internationally. In this Special Issue, we have collected a series of papers describing key long-term (>10 years) Arctic wildlife research and monitoring sites (mainly in Nunavut), including why they were developed, their scientific accomplishments, their impact on domestic or international policy, the relevance of the research to local Inuit communities, and how they might evolve to address future conservation issues. In this overview paper, we describe the social, political, and scientific context underlying the development and delivery of science at these sites over the past 40 years, and comment on the role of these programs in supporting the evolution of wildlife research in Arctic Canada.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".