Parametric estimation and comparison of age-reading error matrices across species, stocks, and calcified structures
Bibliographic record
Abstract
Stock assessments are often based on age-structured data obtained by interpreting calcified structures. Due to readability and human error, the observed age may be wrong. We propose a parametric model for age-reading error matrices, which is more realistic and robust than the commonly used empirical matrices. The parameters have meaningful interpretations, allowing for direct comparison of age-reading properties. We compare different species (Atlantic mackerel ( Scomber scombrus) and herring ( Clupea harengus)), stocks (North Sea autumn-spawning vs. Norwegian spring-spawning herring), and calcified structures (otoliths vs. scales). Three out of four data sets had an asymmetry tendency towards reading higher ages than the true age. The estimated probability of reading the wrong age was lower for scales than for otoliths. The true age is often unknown and assumed to be the modal age. We assess the systematic bias due to this assumption. Finally, when including age-reading error in stock assessment, the dominating age classes were estimated to be larger and spawning stock biomass lower. Our study contributes with methods and insight for including age-reading error in stock assessment.
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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 | 0.027 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".