Using benthic recruitment densities to forecast fisheries recruitment of American lobster in Atlantic Canada
Bibliographic record
Abstract
There is limited understanding of the mechanisms underlying the marked spatial and temporal trends in landings of American lobster in Canada since the mid-1980s, and little ability to forecast changes. We built an individual-based model for three Lobster Fishing Areas (LFAs) in Canada that uses the annual density of benthic recruits in nursery grounds to generate an index of exploited biomass 6 to 10 years in the future, using von Bertalanffy growth equations complemented with inter-individual variability in growth, and regional maturity ogives and spawning probabilities to allow exclusion of ovigerous females from the exploitable biomass. We found significant positive relationships between the modelled biomass index and fisheries landings in all three LFAs. Out-of-sample validation in two LFAs (longer time series) using 1-year and 6-year reduced data sets revealed mean annual prediction errors of 11.4% and 11.2%, and 23.7% and 51.7%, respectively. Our findings strongly suggest benthic recruitment indices could help make strategic decisions concerning lobster fishing activities, and they argue for the continued and expanded monitoring of lobster benthic recruitment in Canada.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".