A simulation study to evaluate impacts of uncertainty on the assessment of American lobster fishery in the Gulf of Maine
Bibliographic record
Abstract
The status of the American lobster (Homarus americanus) in the Gulf of Maine (GOM) has been assessed for the last two decades by comparing an estimated current fishing mortality rate for females (F cur ) with a deterministically estimated biological reference point, F 10% . The most recent assessment determined that GOM had exceeded the F 10% for every year calculated, although in this time landings and abundance have doubled. The current policy does not consider uncertainty in the assessment. This study evaluates the impacts of uncertainty in F 10% and F cur on the status assessment of lobster fishery. Using a Monte Carlo simulation approach, we found that the status assessment would be influenced by uncertainties in estimating F 10% and F cur , and by the choice of decision confidence level reflecting the level of risk managers would like to take. A large uncertainty in F cur and F 10% and a high decision confidence level reduce the likelihood of defining the stock as overfished, and vice versa. Our results suggest that the probability of lobster overfishing may be less than previously thought, and that uncertainty in F cur and F 10% should be quantified and considered in determining the status of the GOM lobster stock.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".