Balancing exploitation and conservation of the eastern Scotian Shelf ecosystem: application of a 4D ecosystem exploitation index
Bibliographic record
Abstract
Abstract Few indicators exist that measure the effects of fishing at the whole-ecosystem level. A 4D ecosystem exploitation index is proposed that integrates four measures of ecosystem status: (i) the trophic balance of exploitation, (ii) the intensity of exploitation, (iii) species richness, and (iv) disturbance of the trophic structure. The central concept is that fisheries should extract yield in proportion to the productivity at each trophic level, at sustainable exploitation rates, with minimal disturbance to the trophic structure, and with no loss in species richness. The ecosystem exploitation index is tested on simulated ecosystem data and applied to data from the eastern Scotian Shelf, Canada. The model simulations provided consistent results that should facilitate cross-system comparisons of exploitation and ecosystem status. When applied to actual observations, the results were less coherent, likely attributable to lag effects and environmental impacts. All fisheries likely alter the trophic structure of ecosystems, and the rate and trophic pattern of exploitation determines the level of change. Exploiting all trophic levels equally would always result in less change to trophic structure than top-heavy exploitation. Further testing is required to determine lag effects and sensitivity to various assumptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".