COMPUTING AND SELECTING AGEING ERRORS TO INCLUDE IN STOCK ASSESSMENT MODELS OF PACIFIC SARDINE (SARDINOPS SAGAX)
Bibliographic record
Abstract
From 2007 to 2010, Pacific sardine stock assessments relied on traditional methods to compute and include ageing errors in the integrated assessment model, Stock Synthesis (SS). Traditional methods assumed that all age readers were unbiased and estimated ageing imprecisions by averaging across all fish that were assigned a given age a by one or more readers. In this study, we used the Ageing Error Matrix (Agemat) model to compute ageing imprecisions, based on classification matrices that quantified the probability of a fish of true age a to be assigned an age a or some other age a', P(a'|a). Using sardine samples collected from Mexico to Canada and aged in five labora tories, we compared three Agemat models, assuming that: (1) the most experienced reader from each laboratory was unbiased (model A); (2) no bias but different stan dard deviation (SD) at age among readers (model B); and (3) no bias, but similar standard deviation at age among readers (model C). We evaluated the performance of this model using the Akaike information criterion corrected for finite sample sizes. Sardine ages ranged from 0 to 8, with increasing reader SD with age. Model C performed better than models A and B, across all data sets and laboratories, and thus was recommended for including ageing imprecisions in sardine assessment models. However, the observed differences in SD across ages and readers called for a better standardization of ageing protocols among laboratories and for applying new methods to reduce potential bias in estimating the oldest age classes.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".