The cost of overfishing and management strategies for new fisheries on slow-growing fish: orange roughy (<i>Hoplostethus atlanticus</i>) in New Zealand
Bibliographic record
Abstract
The history of orange roughy (Hoplostethus atlanticus) stocks, primarily in New Zealand and Australia, is commonly used as an example of the inability to manage fisheries resources. We review the history and status of the New Zealand orange roughy fishery and show that the total loss of potential biological yield from overfishing is no more than 8.3% (1260 tonnes (t)·year 1 ) of the potential yield. The losses from underfishing are estimated to be 810 t·year 1 . We consider the biological and economic consequences of alternative management approaches to the New Zealand orange roughy fishery. We suggest that given the uncertainty in stock abundance and productivity and market and processing capacity limits, the management of New Zealand orange roughy stocks has been close to economically optimal and has produced near maximum sustainable yield from the resource.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".