Accounting for uncertainty due to data processing in virtual population analysis using Bayesian multiple imputation
Bibliographic record
Abstract
Virtual population analysis (VPA) is used in many stock assessment settings and requires a total catch-at-age data set where an age is assigned to each fish that has been caught. These data sets are typically constructed using ad hoc methods that rely on numerous assumptions. Although approaches are available to account for observation error in these data, no statistically rigorous methods have been developed to account for uncertainty from data processing. To address this, we investigated a Bayesian multiple imputation approach to filling missing size data. Using Atlantic yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus obesus) as case studies, we evaluated the hypothesis that data processing is as important in determining management reference points in stock assessments as conventional sources of uncertainty. Size imputation models accounting for location, season, and year provided good predictive capacity. Uncertainty from data processing could be large; however, the circumstances for this were unpredictable and varied depending on the stock. These results indicate that VPA assessments should attempt to account for uncertainty in data processing to avoid potentially large compression of uncertainty in assessment results.
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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 | 0.089 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".