Effects of alternative control rules on the conflict between a fishery and a threatened sea lion (<i>Phocarctos hookeri)</i>
Bibliographic record
Abstract
In New Zealand, a fishery for squid (Nototodarus sloanii) incidentally catches a threatened sea lion, Phocarctos hookeri. Bycatch is managed with an annual limit designed to ensure rebuilding of the sea lion population. We explore the conservation and cost effects of the current limit and two simple alternative rules, comparing them with no fishing and unrestricted fishing. We fitted an age-structured Bayesian model to sea lion pup estimates to obtain samples of the joint posterior distribution of parameters; from these we made 100-year simulations with five harvest control rules under six different sets of environmental conditions. The base-case fit suggests that the current sea lion population may be near its carrying capacity, although this may be sensitive to modelling choices. The fishery bycatch constitutes little risk to the sea lion population in the absence of catastrophes and generates small marginal risks when catastrophes are simulated. The current management rule does not minimise the marginal risk of extinction, is much more costly to the fishery than simple alternative rules, and incurs greatest cost when risk is smallest. The model appears to be a good tool for evaluating alternative management strategies against predefined objectives.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".