Evaluating the impact of log-normal bias-correction on a state-space stock assessment model
Bibliographic record
Abstract
In state-space stock assessment models, recruitment and numbers-at-age are typically modeled as log-normal random variables, with bias correction applied to ensure that their mean matches the expected mean of the random variable. However, it remains unclear whether estimation error in variance parameters, which influence bias correction, propagates to estimates of population quantities. We conducted simulation-estimation experiments to evaluate the effects of bias correction for log-normal random variables and observations. We found that applying bias correction on observations had minimal impact on estimated population quantities, whereas applying bias correction on the process had a significant effect, because estimation error in variance parameters created bias in population estimates. Specifically, when both recruitment deviations and numbers-at-age transitions were treated as random effects, substantial bias in estimated annual recruitments and SSB was found when bias correction was excluded in the operating model but applied in the estimation model. In contrast, not using bias correction had limited negative effects. Thus, we recommend avoiding bias correction for log-normal random variables in state-space models, especially when multiple random-effects processes are modeled simultaneously.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".