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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".