A Review of Science and Conservation Management for the Cumberland Sound Beluga Population
Bibliographic record
Abstract
For many centuries, belugas, or white whales (Delphinapterus leucas), have been a major source of subsistence and cultural identity for the Inuit living along the shores of Cumberland Sound, southeastern Baffin Island. During the late 1800s and first half of the twentieth century, the whales were also heavily exploited commercially for their oil and skins. By the late 1960s and early 1970s it had become clear that the beluga population was greatly reduced from its historical abundance, and efforts began to limit the harvest and monitor the population. The purposes of this paper are to (i) provide a synthesis of developments in Cumberland Sound beluga science and harvest management since 1980 and (ii) describe and discuss efforts to improve the conservation status of the beluga population. Despite large investments in research since the transition to co-management under the Nunavut Agreement, much uncertainty and disagreement remains. Best scientific estimates of current beluga numbers are in the range of 1000 – 1500, with no clear evidence of an increasing or decreasing trend. Officially reported annual landings of harvested whales for the Baffin Island community of Pangnirtung in recent years have ranged between 15 (1993) and 52 (2006), with an average of around 40 whales. Ongoing known or potential threats identified by hunters and scientists include overharvest, ecosystem (including climate-driven) change, interactions with commercial fisheries, predation by killer whales, and stress due to vessel noise. Addressing these issues will require continued research and improved relations between Inuit and the government. Fresh approaches are needed. Newly available analytical and procedural tools may help to overcome longstanding issues that are deeply embedded in cultural and philosophical differences.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".