A PSA for OSA residuals: residual diagnostics for state–space stock assessment models
Bibliographic record
Abstract
We provide a public service announcement on some potential pitfalls of using one-step ahead (OSA) residuals for a state–space assessment model (SSAM). Five residual methods (Pearson, leave-one-out, approximate leave-one-out, OSA, and one-time-step ahead) are tested on a simulated random walk (RW) model with multiple age classes and a production ready SSAM. Multiple cases of model mis-specification were used to demonstrate how the five residual methods were able to detect model mis-specification. For the RW, the Pearson, leave-one-out, and approximate leave-one-out residuals all performed similarly for indicating the mis-specification. For both the RW and production ready SSAM, the choice of how the data were ordered impacted OSA residual patterns, which may be misleading about the source of the mis-specification. We also found that tests of normality for the OSA residuals may not necessarily reveal model mis-specification either.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".