Pacific Herring (<i>Clupea pallasi</i>) in Canada: Generic Framework for Evaluating Conservation Limits and Harvest Strategies
Bibliographic record
Abstract
Abstract Fishery managers often ask how low the abundance of a population can go without causing irreversible harm. The concept of a minimum viable population size is problematic, particularly for pelagic fishes like herring that exhibit high variation in productivity and abundance. However, it should be feasible to determine a conservation limit for population size (N)L that would satisfy stated conservation criteria. For instance, we could choose NL high enough to ensure acceptably low probabilities of triggering World Conservation Union (IUCN) listing criteria (Mace and Stuart 1994) or of falling below a threshold or quasi-extinction level of abundance in the long term. A population reduced to N L should also have an acceptably high probability of recovery to a sustainable target level within a shorter time period. For example, the U.S. Magnuson-Stevens Fishery Conservation and Management Act specifies a legal requirement for recovery within 10 years or as soon as possible (Restrepo et al. 1998) and Johnston et al. (2000) propose a limit to allow recovery within a single generation. Similarly, it should be feasible to determine a corresponding conservation limit for harvest rate (μL). NL and μL values that satisfy these requirements can be determined in Monte Carlo simulations by evaluating outcomes for a wide range of population sizes and harvest rates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".