Modeling population trajectory and probability of decline in northern Hudson Bay narwhals (<i>Monodon monoceros</i>)
Bibliographic record
Abstract
Abstract Narwhals (Monodon monoceros) are an important subsistence harvest species for Inuit communities and their conservation is important for Inuit culture and ecosystem function. The northern Hudson Bay (NHB) narwhal population, which spends summer in northern Hudson Bay, Canada, has been assessed through periodic aerial surveys from 1981 to 2018. To estimate the population trajectory and predict future population trends under various harvest scenarios, a Bayesian population model was fit to four aerial survey estimates and harvest data from 1951 to 2018. The model resulted in a 2019 population estimate of ~14,400, 95% CI [10,300, 20,400] and an estimated starting population of 7,200, 95% CI [1,400, 19,000] in 1951. The model was extended 10 years into the future under three annual harvest scenarios (current harvest: 157, low harvest: 50, and high harvest: 300) and the probability of population decline was estimated. The model predicted a 6% chance of decline with an annual harvest quota of 50 narwhals, 78% for a harvest of 157, and 95% for a harvest of 300. This updated model provides the opportunity to shape conservation efforts by estimating past population trends and how those trends, combined with management action, can affect future population dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".