Conservation and management of Canada’s polar bears (<i>Ursus maritimus</i>) in a changing Arctic<sup>1</sup>This review is part of the virtual symposium “Flagship Species – Flagship Problems” that deals with ecology, biodiversity and management issues, and climate impacts on species at risk and of Canadian importance, including the polar bear (<i>Ursus maritimus</i>), Atlantic cod (<i>Gadus morhua</i>), Piping Plover (<i>Charadrius melodus</i>), and caribou (<i>Rangifer tarandus</i>).
Bibliographic record
Abstract
Canada has an important responsibility for the research, conservation, and management of polar bears ( Ursus maritimus Phipps, 1774) because the majority of polar bears in the world occur within the nation’s borders. Two fundamental and recent changes for polar bears and their conservation have arisen: (1) the ongoing and projected further decline of sea-ice habitat as a result of climate change and (2) the implementation of aboriginal land claims and treaties in Canada’s North. Science has documented empirical links between productivity of polar bear population and sea-ice change. Predictive modeling based on these data has forecast significant declines in polar bear abundance and distribution of polar bears. With the signing of northern land claims and treaties, polar bear management in Canada has integrated local aboriginal participation, values, and knowledge. The interaction of scientific and local perspectives on polar bears as they relate to harvest, climate change, and declining habitat has recently caused controversy. Some conservation, management, and research decisions have been contentious because of gaps in scientific knowledge and the polarization and politicization of the roles of the various stakeholders. With these ecological and governance transitions, there is a need to re-focus and re-direct polar bear conservation in 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".