Robust state space models for estimating fish stock maturities
Bibliographic record
Abstract
Abstract Here, we formulate robust state space models (SSMs) and develop inference tools in the context of fisheries science and management. Our prototype model concerns the maturity of fish by age over time, knowledge of which is fundamental to understanding the dynamics and productivity of fish stocks, a key component in fish stock assessment. Our SSM incorporates dynamics over time and yields robust estimates of the proportion of fish mature at various ages for a collection of cohorts of interest. The estimates are obtained using an iterative weighted likelihood where the high‐dimensional unobserved dynamics of the SSM and the optimization over the fixed parameters are handled with the Automatic Differentiation Model Builder. The data used to both demonstrate and validate our new approach comes from the annual sampling program of Fisheries and Oceans Canada, the primary scientific and regulatory body responsible for stock assessments. In addition, we carried out a comprehensive simulation study to demonstrate the robustness of our approach to realistic contamination. The Canadian Journal of Statistics 43: 133–150; 2015 © 2015 Statistical Society of Canada
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".