Combining multiple data sources to model population dynamics of Eastern Canada-West Greenland bowhead whales
Bibliographic record
Abstract
The Eastern Canada-West Greenland (EC-WG) bowhead whales ( Balaena mysticetus ) were heavily harvested from 1530 to 1915, and the population was depleted to commercial extinction. Obtaining reliable estimates of abundance through aerial surveys can be challenging due to the vast area that needs to be covered. To overcome this, a Brownian bridge movement model (BBMM) was used with data obtained from satellite-tagged bowhead whales. The BBMM allowed the calculation of the probability of occupancy both inside and outside areas surveyed during aerial surveys conducted in 1981, 2002 and 2013. Using a Quasi-Poisson regression, the relationship between the probability of occupancy and abundance in surveyed areas was established. This relationship was then used to extrapolate survey estimates into unsurveyed areas. Extrapolated estimates were included with genetic mark-recapture abundance estimates from 2013 to 2017 and harvest history into a Bayesian stock production model to recreate population dynamics post-commercial whaling, 1915–2022, and project 10 years into the future under various harvest levels (0, 10, 20, 30 whales). The model estimated a 2022 population of 8147 (95 % CI 6152-10,825) whales, and an initial population (N 1915 ) estimate of 817 (95 % CI 225–4194) whales. Calculating the likelihood of population decline indicated probabilities ranging from 12 to 31 % after a 10-year period across all harvest levels. The findings of the model suggest that EC-WG bowhead whales have been consistently recovering following the cessation of commercial whaling and have already surpassed the point of maximum productivity, leading to a slowdown in growth in recent years.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".