An adaptive, rotational harvest strategy for data-poor fisheries on sedentary species: Application to the giant red sea cucumber (parastichopus californicus) in British Columbia
Bibliographic record
Abstract
This research explores an adaptive rotational harvest strategy using animal size and population density as indicators for allowing harvest. Using sea cucumbers as a case study, I evaluate the relative yield and conservation performance of adaptive rotation and annual harvest strategies under a range of scenarios characterising uncertainty in population dynamics and localised harvest rates. In each scenario, the adaptive strategy achieves the rotation period that maximises long-term yield subject to conservation constraints. Under most scenarios and stochastic variability, adaptive rotation resulted in relatively higher spawning biomass and yield than annual harvest, which performed well only under assumptions of high productivity or low harvest rate. The adaptive strategy is robust to uncertainty in harvest rate and population dynamics, adjusting harvest frequency to meet recovery targets. I modelled the use of "insurance areas", or harvest reserves, to guard against there being no harvestable (recovered) areas under a system of adaptive rotation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".