Bibliographic record
Abstract
Eastern Canada-West Greenland (EC-WG) Bowhead whales (Balaena mysticetus) are sustainably harvested by Inuit in Canada at up to seven individuals per year, but the current bowhead whale management approach does not contain a provision for Inuit to carry-over unused strikes to subsequent years. A deterministic population model was used to evaluate whether a provision to carry-over unused strikes could be considered for bowhead co-management. To determine the sensitivity of the model to inputs, a range of parameters were used to assess uncertainty in the population dynamics. Model scenarios were simulated over a 40-year time period that examined the use of 5-year and 10-year allocation blocks with high licence totals (50 per five-year block, 100 per 10- year block). Various carry-over scenarios were used, including front-loading and back loading of harvests, and an extreme case in which all licences could be carried over through the entire allocation period. The results, which were robust to various input parameters, indicate that any of the carry over provisions that were assessed (over 5 or 10-year allocation blocks) would have little impact on the EC-WG bowhead population trajectory. The simulations investigated here do not account for increasing uncertainty related to the impacts on bowhead life history parameters resulting from environmental change (e.g., climate change) and increased anthropogenic activities (e.g., resource development, shipping). Regular population abundance estimates are needed to ensure sustainable harvest advice is consistent with Potential Biological Removal (PBR) assumptions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".