Diagnosing common sources of lack of fit to composition data in fisheries stock assessment models using one-step-ahead (OSA) residuals
Bibliographic record
Abstract
Fisheries stock assessments often include age- and size-composition data to estimate recruitment strengths, mortality rates, and management quantities. Compositions inherently have correlation among the categories, and therefore residuals are not independent. One-step-ahead (OSA) residuals have been proposed as a replacement for the commonly used (but incorrectly interpreted) Pearson residuals; however, there is no clear best practice for diagnosing model fit when using OSA residuals. We use a simple example to illustrate common sources of model-misspecification and impacts on statistical and visual diagnostics. We find that visual inspection of model fit aggregated across all observations reliably identifies many types of misspecifications, visual inspection of Pearson residuals can reveal further lack of fit, and statistical analysis of OSA residuals provides for objective evaluation of both lack-of-fit and overall data weighting. The power to detect model misspecification depends on the sample size, the number of age bins, and the number of years of data. By illustrating common problems when the correct answer is known, this work provides a guideline for model diagnostics using OSA residuals in more complex settings.
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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 | 0.036 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".