Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth
Bibliographic record
Abstract
Age composition is defined as the proportion of a fish population belonging to each age class and is an informative input to stock assessment models. Variations in somatic growth rates may lead to larger errors in age composition estimates. To reduce this source of error, we compared the performance of four methods for estimating age compositions of a simulated fish population: two methods based on age–length keys (ALK, pooled and annual) and two model-based approaches (generalized additive models (GAMs) and continuation ratio logits (CRLs)). CRL was the most robust and precise method, followed by annual ALKs, particularly when significant growth variability was present. We applied these methods to survey age subsample data for Pacific cod (Gadus macrocephalus) in the eastern Bering Sea, estimating age compositions that were then incorporated in its stock assessment model. The model that included age compositions estimated by CRL displayed the highest consistency with other data in the model. CRL approach has utility for estimating age compositions employed in stock assessment models, especially when substantial variation in somatic growth is present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".