Total Abundance and Harvest Impacts on Eastern Hudson Bay and James Bay Beluga 2015–2022
Bibliographic record
Abstract
Belugas from the James Bay population (JAM) and Belcher Islands-Eastern Hudson Bay (BEL-EHB) stock are harvested by hunters from all Nunavik communities and the Nunavut community of Sanikiluaq. In 2020–2021, a total of 366 belugas were reported harvested by Nunavik hunters, including 41 animals harvested in the Long Island area. From those, an estimated 139 BEL-EHB animals were harvested. Another 19 BEL-EHB animals were harvested in Sanikiluaq. A population model fitted to a time series of 8 aerial survey estimates using Bayesian methods and taking into account removals by harvesters provided a 2021 abundance estimate of 16,700 belugas in James Bay and a range of 2,900–3,200 belugas in eastern Hudson Bay, depending on model assumptions. The James Bay population has levelled off since the last assessment, whereas the BEL-EHB stock is currently declining at a rate of 2.5% per year. A harvest of 190 belugas per year in James Bay, would result in a 50% probability of decline in the JAM population after 5 years. The Potential Biological Removal (PBR) for this population is 296 belugas. If a Precautionary Approach framework was used to manage beluga in James Bay, a range of 170-173 belugas could be harvested annually. For the BEL-EHB stock, two model runs were completed and harvests were evaluated against two benchmarks or thresholds over time frames of 5 and 10 years. Depending on model assumptions, benchmarks and timeframes, harvests should not exceed levels of 0–70 BEL-EHB belugas annually for the stock to remain above the benchmark abundance estimate. The PBR for this stock is 5 animals. Over a 50-year time period, if the annual harvest of beluga from the BEL-EHB stocks stays within 20-25 animals annually, then there is a high probability of staying above the precautionary reference level.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".