Biological reference points for American lobster (<i>Homarus americanus</i>) populations: limits to exploitation and the precautionary approach
Bibliographic record
Abstract
Large-scale changes in American lobster (Homarus americanus) landings and abundance have been documented in both Canada and the United States over the last several decades. The spatial coherence of these changes suggests the importance of common environmental and fishery-related factors operating over broad areas in the western North Atlantic. Changes in both biotic and abiotic factors have been hypothesized to underlie the recent increases in lobster production. Area expansion of lobsters to previously unoccupied or low-density areas appears to be an important element of the population increase. Here, we review biological reference points applied to American lobster populations in the United States and Canada. Egg production per recruit models have been used to specify limit reference points (F10% in the United States) or target reference points (increasing egg production per recruit to twice its 1995 level in Canada). Surplus production and yield-per-recruit models have also been employed to provide qualitative management guidelines. We describe sources of uncertainty in the development of biological reference points for American lobster based on dynamic pool models in relation to the precautionary approach. Finally we consider auxiliary indicators and reference points with potential application to lobster stocks.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".