Ecosystems say good management pays off
Bibliographic record
Abstract
Abstract Understanding the strengths and weaknesses of alternative assessment methods, harvest strategies and management approaches are an important part of operationalizing single‐species and ecosystem‐based fisheries management. Simulations run using two variants of a whole‐of‐ecosystem model for the Southern and Eastern Scalefish and Shark Fishery ( SESSF ) area shows that (a) data‐rich assessments outperform data‐poor assessments for target species and that this performance is reflected in the values of many system‐level ecosystem indicators; (b) ecosystem and multispecies management outperforms single‐species management applied over the same domain; (c) investment in robust science‐based fisheries management pays dividends even when there are multiple jurisdictions, some of which are not implementing effective management; and (d) that multispecies yield‐oriented strategies can deliver higher total catches without a notable decline in overall system performance, although the resulting system structure is different to that obtained with other forms of ecosystem‐based management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".