Assessing the sensitivity and specificity of fish community indicators to management action
Bibliographic record
Abstract
We assessed ten trophodynamic indicators of ecosystem status for their sensitivity and specificity to fishing management using a size-resolved multispecies fish community model. The responses of indicators to fishing depended on effort and the size selectivity (sigmoid or Gaussian) of fishing mortality. The highest specificity against sigmoid (trawl-like) size selection was seen from inverse fishing pressure and the large fish indicator, but for Gaussian size selection, the large species indicator was most specific. Biomass, mean trophic level of the community and of the catch, and fishing in balance had the lowest specificity against both size selectivities. Length-based indicators weighted by biomass, rather than abundance, were more sensitive and specific to fishing pressure. Most indicators showed a greater response to sigmoid than Gaussian size selection. Indicators were generally more sensitive at low levels of effort because of nonlinear sensitivity in trophic cascades to fishing mortality. No single indicator emerged as superior in all respects, so given available data, multiple complementary indicators are recommended for community monitoring in the ecosystem approach to fisheries 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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".