Evaluation of a Provision to Carry Over Unused Licences for Eastern Canada-West Greenland Bowhead Whales (Balaena mysticetus) in Canada
Bibliographic record
Abstract
In response to Resource Management’s request for Science Advice on a proposed system of flexible licence allocations for Eastern Canada-West Greenland (EC-WG) bowhead whales (Balaena mysticetus), population model scenarios were simulated to estimate the sustainability of Canadian license carry-overs or credits to subsequent years. Methods to explore the sustainability of flexible catch limits were similar to those applied to narwhal (Monodon monoceros), beluga (Delphinapterus leucas), and Atlantic walrus (Odobenus rosmarus rosmarus) catch limits, but simplified by using a fixed number of licences available per allocation block rather than updating allowable catch limits based on population status. As a communal venture with logistical challenges, the unique nature of bowhead whaling will likely limit licence demand compared to other harvested marine mammals that have catch limit quotas (e.g., narwhal). A deterministic Pella-Tomlinson logistic growth population model was used to explore various harvest scenarios, including no harvest and a fixed Potential Biological Removal (PBR) level of 52 whales annually. These models, simulating population trajectories over 100 years, provide confidence that harvest at PBR (an order of magnitude higher than current subsistence harvests) will have no major effects on population recovery at a range of initial population sizes (N0) and carrying capacity (K) estimates that reflect available knowledge of bowhead whale status. Under the model scenarios, carry-over provisions at current annual licence limits (ca. 6 per year) should have little to no impact on EC-WG bowhead population status over the long term. These results informed additional corroborative modelling for a 40-year time period to examine the use of 5-year and 10-year allocation blocks with moderately high (compared to current demand) licence totals (i.e., 50 per five-year block, 100 per 10-year block). Various carry-over scenarios were explored, including an extreme case in which all licences (n = 50 or 100) could be carried over through the entire allocation period. Other scenarios included front loading and back-loading of harvests, with all quota (50 or 100 whales) taken in the first or last year of each 5 or 10-year block. Licence (and harvest) carry-over had little impact on simulated EC-WG bowhead whale population growth trajectories under the model assumptions used, and harvests at current and slightly higher levels are expected to be sustainable with the implementation of a flexible licence allocation system that allows carry-over. This advice is dependent on a number of assumptions regarding current and historic bowhead abundance, population biology, and ecosystem condition. As better information becomes available these models can be revisited, but based on our present level of understanding, licence carry-over provisions can improve resource access for Inuit while allowing continued population growth.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".