Current usage of fisheries indicators and reference points, and their potential application to management of fisheries for marine invertebrates
Bibliographic record
Abstract
The use of indicators in management of invertebrate resources is placed in the context provided by more extensive applications in finfish fisheries. Indicators proposed for the Convention on International Trade in Endangered Species based on extent-of-decline and trend analysis are appropriate should full assessments be unavailable. Measuring reproductive performance frequently builds on egg-per-recruit considerations, given that age structure and stock–recruit relationships are rarely available. Reference points derived from models are compared with direct use of data series, and a broad-brush approach providing a redundancy of indicators is recommended. Indicators may measure productivity as well as biomass and exploitation rate, but ecosystem, spatial, habitat, environmental characteristics, and socio economic considerations also require monitoring. There is a need to integrate multiple indicators and limit reference points into harvest rules and other decisional infrastructures. The various driving force – pressure – state – impact – response classifications of indicators in use for environmental assessment are now being proposed for marine resources and offer one context for combining multiple indicators. Another is provided by the traffic light approach already used for invertebrate fisheries. The use of indicators and reference points in stock rebuilding is described.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".