Evaluating resiliency for American lobster and its fishery in a changing environment
Bibliographic record
Abstract
The application of individual-based modeling frameworks has expanded over the last few decades, but their use is still limited in global fisheries management practices. These probabilistic models allow simulation of complex species’ life history and fishery-dependent processes. This research sees the advancement and implementation of the individual-based lobster simulator, an individual-based modelling framework, to simulate American lobster ( Homarus americanus) stock dynamics in the Gulf of Maine and Georges Bank region under a suite of climate change and management scenarios. With impacts of warming waters directly considered, the stock was projected out to equilibria under scenarios of status quo management and alternative future management schema. This analysis allowed for evaluation of the effectiveness and overall impact of alternative regulatory measures on the stock for a given climate change scenario. We conclude that regulatory changes were generally effective at maintaining spawning biomass and legal biomass under climate scenarios but were less effective at maintaining recruitment and landed catch. Furthermore, a given management action had less effectiveness at maintaining stock levels under increased levels of climate change.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".