Developing robust frequentist and Bayesian fish stock assessment methods
Bibliographic record
Abstract
Abstract Errors in fitting models to data are usually assumed to follow a normal (or log normal) distribution in fisheries. This assumption is usually used in formulating likelihood functions often required in frequentist and Bayesian stock assessment modelling. Fisheries data are commonly subject to atypical errors, resulting in outliers in stock assessment modelling. Because most stock assessment models are nonlinear and contain multiple variables, it is difficult, if not impossible, to identify outliers by plotting fisheries data alone. Commonly used normal distribution‐based frequentist and Bayesian stock assessment methods are sensitive to outliers, resulting in biased estimates of model parameters that are vital in defining the dynamics of fish stocks and evaluating alternative strategies for fisheries management. Because of the high likelihood of having outliers in fisheries data, frequentist or Bayesian methods robust to outliers are more desirable in fisheries stock assessment. This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods. Using simulated fisheries as examples, we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in fisheries data and compare the robust approaches with the commonly used normal distribution‐based approach. The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".