Contrasting deterministic and probabilistic ranking of catch quotas and spatially and size-regulated fisheries management
Bibliographic record
Abstract
Large uncertainties over the dynamics of resource systems have increasingly led to the use of probabilistic modeling in the provision of model-based fishery management advice. However, deterministic analysis still remains the easiest and quickest approach to formulate model-based management advice. Here, we contrast deterministic and probabilistic modeling methods in evaluations of the potential consequences of alternative fishery management measures such as spatial and temporal closures and size-specific regulations. We thereby assess how model-based fishery management advice may vary between deterministic and probabilistic analyses of system dynamics. Using data for the sandbar shark (Carcharhinus plumbeus) population off the eastern coast of the USA, it is shown that under a variety of conditions, the use of management measures that provide protection to specific age groups of a population, such as size limits, might be less effective in achieving stock recovery of slow-growing, late-maturing, highly mobile species than catch quotas. It is also shown that management approaches that, according to deterministic calculations, appear to be the most effective are not so when uncertainty in the population dynamics is taken into account.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".