Estimating economic-based target reference points for key species in multi-species multi-métier fisheries
Bibliographic record
Abstract
Consideration of economic outcomes is commonplace in most fisheries management systems globally, although only a few jurisdictions have adopted an economic objective as the primary target for fisheries management. Such an objective has been adopted for Australia's federally managed fisheries, with maximum economic yield (MEY) identified as the primary management objective. Correspondingly, target reference points defined in terms of biomass (i.e., BMEY) are used in harvest control rules. In the absence of explicit BMEY estimates, proxy estimates based on maximum sustainable yield (i.e., BMSY) are used. Identifying BMEY in multi-species fisheries is complicated as most stock assessments are undertaken at the individual species level, but economic activity occurs across species. This is further complicated when different fishing activities using different fishing gears and targeting practices (i.e., métiers) are present in a fishery. We employ an age-structured bioeconomic model to estimate BMEY for key species in a multi-species, multi-métier fishery. We find that optimal biomass levels are substantially higher than those assumed under the current proxy-based system, and that the economic targets are sensitive to prices and fishing costs, both of which change over time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".