Bridging fisheries management and biodiversity conservation norms: potential and challenges of balancing harvest in ecosystem-based frameworks
Bibliographic record
Abstract
Abstract Balanced harvesting has been proposed as a way for fisheries management to achieve the requirements of both the Law of the Sea Convention (LOSC)—to maintain stocks at the level at which they could produce MSY—and the Convention on Biological Diversity (CBD)—to maintain ecosystem structure and functioning. This paper examines these requirements and briefly presents four system-level relationships (spectra), representing ecosystem structures that might guide management decision-making aiming to meet both requirements. These spectra would fit in the widely accepted frameworks of the Ecosystem Approach enshrined in the CBD and adopted by FAO for Fisheries. A size spectrum, relating biomass to body length, is used as an example to illustrate its potential to support management decision-making—much like present stock-based harvest control rules—in more ecosystem-compliant fishing strategies at a sector or ecosystem level, as a complement to those currently used at a stock/population level.
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 | 0.075 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".