Estimating catch-at-age by combining data from different sources
Bibliographic record
Abstract
Estimating the catch-at-age of commercial fish species is an important part of the quota-setting process for many different species and almost all countries with a fishing fleet. Current procedures are usually very time-consuming and somewhat ad hoc, and the estimates have no measure of uncertainty. We previously developed a method for catch-at-age of Norwegian Atlantic cod (Gadus morhua), but this only considered aged fish sampled randomly from random hauls. In most countries, the sampling scheme is not so simple. There are usually a very large number of length-only samples from which the age must be estimated using an agelength relationship, and often some or all of the age samples are collected from data that are first stratified by length. This adds considerably to the difficulties in the estimation. In this paper, we model the three different kinds of data simultaneously using a development of our earlier Bayesian hierarchical model. This enables us to obtain estimates of the catch-at-age with appropriate uncertainty and also to provide advice on how best to sample data in the future. The data types are random samples of age, length, and weight; age and weight stratified by length; and length only.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".