Revised Atlantic seal management strategy
Bibliographic record
Abstract
Since its adoption in 2003, the Atlantic Seal Management Strategy (ASMS) Precautionary Approach (PA) framework has been used to provide advice on sustainable harvest levels for Atlantic seals and other marine mammal stocks in Canada. To account for differences in the current state of knowledge and the level of uncertainty associated with estimates of stock status, two categories are distinguished within the ASMS, ‘Data Rich’ and ‘Data Poor’. For a stock considered Data Rich, harvest advice is made using a population model that describes the dynamics of the stock. Under the ASMS Data Rich PA framework, five recommendations were made, including to use estimated environmental carrying capacity (K) instead of maximum population size observed or estimated (Nmax) for the reference abundance level (Nref) where K can be reliably estimated. Changes were also made to the timeframes used for the Harvest Control Rules (HCRs) in the Healthy (15 years) and Cautious Zones (1.5 generations) in keeping with the PA. Simulations will be required to evaluate whether or not these timeframes are appropriate for Atlantic seals under the established risk tolerance levels for preventable decline under the PA Policy. For a stock considered Data Poor, total removals are estimated using the Potential Biological Removal (PBR) approach which is a product of three parameters: a minimum estimate of abundance (Nmin), one-half of the maximum intrinsic rate of population growth (Rmax), and a recovery factor (FR). Under the ASMS Data Poor PA framework, five recommendations were made for refining PBR calculation, including guidelines on estimating Nmin, the value to be used for Rmax, and the criteria for the selection of the FR. Both the Data Rich and Data Poor PA frameworks described in the ASMS are consistent with the intent of the DFO PA Policy. The ASMS was revised to fill gaps and clarify details from the previous approach. The revised ASMS PA framework can be generalized to extend to other marine mammal stocks in Canada. Variations in the timeframes for the HCRs could be evaluated using simulations for marine mammals with different life histories.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".