System-level optimal yield: increased value, less risk, improved stability, and better fisheries
Bibliographic record
Abstract
The discipline and practice of fisheries science and management have had an useful, successful, and interesting history. The discipline has developed over the past century and a half into a very reductionist, highly quantitative, socially impactful endeavor. Yet given our collective successes in this field, some notable challenges remain. To address these challenges, many have proposed ecosystem-based fisheries management that takes a more systematic approach to the management of these living marine resources. Here I describe systems theory and associated constructs underlying system dynamics, elucidate how aggregate properties of systems can and have been used, contextualize these aggregate features relative to optimal yield, and note how this approach can produce useful estimates and outcomes for fisheries management. I explore two contrasting examples where this approach has and has not been considered, highlighting the benefits of applying such an approach. I conclude by discussing ways in which we might move forward with a portfolio approach for both the discipline and practice of fisheries science and 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".