Northern Hudson Bay narwhal abundance estimates
Bibliographic record
Abstract
To estimate current abundance and determine trends in population dynamics of Northern Hudson Bay (NHB) narwhal, a population model was fit to four survey estimates from 1982– 2018 and a series of reported annual harvests from 1951–2018. Earlier surveys in the series (1982 and 2000) were conducted and analysed using different protocols than the more recent surveys in 2011 and 2018. The estimates from these earlier surveys were adjusted to account for different analyses and survey methods to make them comparable. The model was robust to input parameters, and estimated a 2019 abundance of 14,400 (95% CI 10,300–20,400 [rounded to the nearest hundred]) narwhal. Based on the model trajectories, a total landed catch of 0, 63, 83, 93, 108, 173, and 450 narwhal per year would result in a 0%, 20%, 40%, 50%, 60%, 80%, and 100% probability of decline, respectively, in this NHB narwhal population in ten years. Potential Biological Removal (PBR) from the modelled 2019 abundance estimate was calculated to be 188, resulting in a landed catch of 151 to account for whales killed but not landed
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".