Bayesian integrated survey-based assessments: an example applied to North Sea herring (<i>Clupea harengus</i>) survey data
Bibliographic record
Abstract
Stock assessments that use only fisheries independent data have been developed from a number of different standpoints over the last 15 years. While the ability of such stock assessments to avoid the use of potentially compromised or hard to interpret commercial data or making certain assumptions that are common to traditional assessments has been established, little has been made of their potential for detecting and estimating complex mortality trends over time or their potential utility in survey-based management procedures. Using North Sea herring ( Clupea harengus ) data as an example, a Bayesian survey-based assessment method that is able to estimate all the key population variables is detailed. However, survival probability, and not fishing mortality that is conditional on natural mortality, is the key parameter. Reversible jump Markov chain Monte Carlo routines were developed to explore the range of ages over which survival separates into year and age effects (a common assumption in many stock assessments). Post hoc estimates of natural mortality suggest that changes over years and ages may have occurred in relativity to historic levels. The derivation of reference points based on survival probability and surplus biomass production are detailed as proxies for more common F-based reference points. The potential role for the outputs of such assessments in a management procedure sense is discussed.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".