Model averaging and probable status of North Pacific striped marlin, Tetrapturus audax
Bibliographic record
Abstract
We show how model averaging can be applied to estimate the probable status of a fishery resource under assessment scenario uncertainty. This approach is applied to North Pacific striped marlin ( Tetrapturus audax ), an apex predator that may be vulnerable to recruitment overfishing in pelagic longline fisheries targeting tunas. In the current striped marlin assessment, two assessment scenarios were used to account for different hypotheses about the steepness of the stock–recruitment dynamics. Estimates of spawning stock and recruitment from these scenarios are used post hoc to fit age-structured production models that represent alternative hypotheses about the degree of compensation in stock–recruitment dynamics and the degree of serial correlation of environmental forcing. Model-averaged estimates of target spawning biomass to produce maximum sustainable yield (SMSY) and the associated limit fishing mortality (FMSY) characterize relative stock status (S/SMSY and F/FMSY) under each scenario. Scenario-weighted averages of relative status determine probable stock status, with weightings reflecting the credibility of each scenario. Estimates of the variance of probable status account for both model selection and assessment scenario uncertainty in risk analyses. Using model averaging to estimate probable stock status from multiple assessment scenarios is analogous to using ensemble averages from multiple predictive models to make weather forecasts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".