Modelling population abundance and future trajectories for Arctic marine mammals to inform Canadian sustainable fisheries management
Bibliographic record
Abstract
Arctic cetacean species, beluga whales (Delphinapterus leucas), narwhals (Monodon monoceros), and bowhead whales (Balaena mysticetus), have a long history of subsistence harvest by northern communities, as well as various levels of exploitation from historical commercial whaling. To achieve sustainable subsistence harvest, Arctic cetacean populations must be monitored to inform management decisions. Three populations: Northern Hudson Bay (NHB) narwhal, Cumberland Sound (CS) beluga, and Eastern Canada-West Greenland (EC-WG) bowhead whales, all pose various challenges associated with building population dynamics models for management. The NHB narwhal population presents challenges surrounding inconsistent abundance estimates which limit their ability to be included into a model, this was addressed by comparing abundance estimate methodologies and calculating correction factors to adjust older estimates accordingly. The CS beluga population is endangered and is assumed to be affected by the effects of climate change, thus, the challenge of incorporating environmental variables into a population dynamics model was addressed with this population. The EC-WG bowhead whales present a challenge with achieving an abundance estimate, as their vast range makes aerial surveys, the typical method for marine mammal abundance estimation, difficult. Instead, genetic mark-recapture analyses using biopsy samples were used to estimate EC-WG bowhead whale abundance. To model population dynamics of EC-WG bowhead whales the challenge of underestimated abundance from insufficient aerial survey coverage was addressed by using telemetry data in a utilization distribution map to extrapolate abundance. Challenges associated with determining population dynamics of Arctic cetaceans can be addressed with creative problem solving, considering the types of data available and specific management goals associated with each population of focus.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".