Progress in the use of ecosystem modeling for fisheries management
Bibliographic record
Abstract
INTRODUCTION We are moving toward ecosystem-based management of fisheries, and it is clear that ecosystem modeling is an important tool for evaluating scenarios and trade-offs as part of such a move. This chapter evaluates the extent of ecosystem modeling as an active research field, the potential usefulness of the models for fisheries management, and actual use of ecosystem models in fisheries management. In addition we present some recommendations for how the move toward ecosystem-based management can be supported through an adaptive environmental assessment and management process. It is important at the outset to be careful about what it means to “use” a model in fisheries management. At one extreme, imagine taking model predictions at face value and applying them blindly in setting reference points and regulations; some managers seem to hope for or expect such models to appear, presumably as absolution or excuse for not making thoughtful choices in the face of uncertainty. No fisheries model, whether a highly “precise” single-species assessment or a crude ecosystem biomass flow scheme, will ever be reliable enough to use in such an uncritical way, if for no other reason than unpredictability in environmental conditions. At the opposite extreme, we can certainly use even very simple trophic interaction calculations to screen qualitative policy options and answer very basic questions raised in management settings (e.g., could predation rates by some particular predator that fishers do not like be high enough to ever justify a culling policy?).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".