Evaluating management strategies for marine mammal populations: an example for multiple species and multiple fishing sectors in Iceland
Bibliographic record
Abstract
A management strategy evaluation (MSE) is used to estimate success at achieving conservation goals for marine mammals while also aiming to minimize impacts on commercial fisheries. It is intended to improve understanding of US import rules that require countries exporting fish and fish products to the USA to adhere to marine mammal bycatch standards “comparable” to those used by the USA. The MSE framework is applied, for illustrative purposes, to export fisheries in Iceland that impact harbor porpoises (Phocoena phocoena), harbor seals (Phoca vitulina), and grey seals (Halichoerus grypus). Several management strategies are evaluated. The harbor porpoise population is estimated to be close to or above its maximum net productivity level (MNPL) and, according to the model, will continue to increase even if current levels of human-caused mortality are unchanged. In contrast, the grey seal and harbor seal populations are below MNPL, and bycatch mortality in the lumpfish (Cyclopterus lumpus) fishery will need to be reduced to allow them to recover to MNPL.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".