A state-space approach for detecting growth variation and application to North Pacific groundfish
Bibliographic record
Abstract
Understanding demographic variation in recruitment and somatic growth is key to improving our understanding of population dynamics and forecasting ability. Although recruitment variability has been extensively studied, somatic growth variation has received less attention, in part because of difficulties in modeling growth from individual size-at-age estimates. Here we develop a Bayesian state-space approach to test for the prevalence of alternative forms of growth rate variability (e.g., annual, cohort-level, or in the first year recruited to the fishery) in size-at-age data. We apply this technique to 29 Pacific groundfish species across the California Current, Gulf of Alaska, and Bering Sea – Aleutian Islands marine ecosystems. About 40% of modeled stocks were estimated to exhibit temporal growth variation. In the majority of stocks, growth trends fluctuated annually across ages in a single year, suggesting that either there are shared environmental features that dictate growth across multiple ages or the presence of some systematic (within-year) observation errors. This method represents a novel way to use size-at-age data from fishery or other sources to test hypotheses about growth dynamics variability.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".