Using an individual-based model to simulate the Gulf of Maine American lobster (<i>Homarus americanus</i>) fishery and evaluate the robustness of current management regulations
Bibliographic record
Abstract
Individual-based models (IBMs) can capture complex processes with a flexible probabilistic approach, which makes them useful for studying organisms with complex life history and fishery processes such as the American lobster (Homarus americanus). This research aims to modify and parameterize an individual-based lobster simulator (IBLS) to simulate the American lobster fishery in the Gulf of Maine. To simulate the fishery, the IBLS was tuned to match the seasonal catch and size composition from the 2015 American lobster stock assessment by adjusting the values of coefficients for select parameters. With appropriate coefficients for the initial abundance, recruitment, and seasonal encounter probability levels, the tuned IBLS accurately simulated the historical landings. Given the uncertainty in future American lobster recruitment, the tuned IBLS was then used to evaluate the effectiveness of current management regulations under different levels of recruitment.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".