Adding the risk of stock collapse over time to stock assessments and harvest allocation decisions
Bibliographic record
Abstract
Abstract Globally, many fisheries have experienced collapse even though most of these fisheries had management plans with harvest control rules and were supported by scientific modelling that explicitly accounted for uncertainty. Recognizing that an informed decision on risks of a stock collapse versus harvest is only possible when the outcomes of the technical measures are described explicitly. We propose that the cumulative probability of stock collapse over a range of harvest levels would provide a perspective of the future consequences of harvesting decisions. Adding to the harvest level negotiations the consideration of how long a fishery should sustain the livelihoods of fishers may provide managers, fishers, and other stakeholders with a more tangible understanding of the risks within the context of precautionary principles in decision-making. We use a time series from the Canadian Cod fishery of the Southern Gulf of St. Lawrence, from which we construct and calibrate a simplified model as an emulator of more comprehensive models to demonstrate the approach. The implications of adding an analysis of the probabilities of stock collapse for a range of harvest levels and using a risk matrix to inform decision-making are discussed for four selected years 1974, 1986, 1993, and 2017.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".