Which community indicators can measure the impact of fishing? A review and proposals
Bibliographic record
Abstract
Population and community descriptors that might be used as indicators of the impact of fishing are reviewed. The criteria used for the evaluation of these indicators are meaning, expected effect of fishing, exclusiveness to fishing effects, and measurability. Population indicators such as total mortality rate, exploitation rate, or average length are the most operational indicators because their meaning is clear and the expected effect of fishing on them is well understood so that reference points can be set. On the other hand, indicators based on the composition of species assemblages such as diversity indices and ordination of species abundances are difficult to interpret, and the effect of fishing on them is not easily predicted. Robust indicators describing the community functions of interest (production and transfer of biomass to large fish), such as size spectra descriptors or the proportion of piscivorous fish in the community, are more promising but are not yet well developed. New candidate indicators are proposed: the change in fishing mortality required to reverse population growth rate, the proportion of noncommercial species in the community, and the average length and weight in the community.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".