Meta-population Modeling of Narwhals, Monodon monoceros, in East Canada and West Greenland
Bibliographic record
Abstract
Narwhals, Monodon monoceros, in the Baffin Bay region of East Canada and West Greenland are aggregated into eight summer stocks, and they are hunted in 24 geographically and seasonally separated hunts. Several of the hunts target animals from different stocks, making sustainable management challenging. We develop a meta-population model to face the challenge. We use a catch allocation model to allocate the catches in the different hunts into historical time-series distributions for the total removals from each stock. Bayesian population dynamic modeling then analyzes the impacts of the 24 hunts on the eight stocks. The catch allocation, however, depends on the population dynamics. Thus, we integrate the allocation and population modeling in iterative runs until the estimated catch histories and population trajectories converge. This framework is useful for sustainability assessment and management practices. The allocation model assigns catches to stocks, and the population modeling estimates sustainability for each stock. When done in retrospect it judges the sustainability of current takes. Acting on these measures, managers can use forward projections to identify overall hunting patterns that secure sustainability for all narwhals in the Baffin Bay region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".