A Bayesian approach for overcoming inconsistencies in mortality estimates using, as an example, data for <i>Acanthopagrus latus</i>
Bibliographic record
Abstract
Reliable estimates of natural (M) and total mortality (Z) are essential for effective fisheries management. However, estimates of M, which are frequently determined from life history parameters, are imprecise and often inconsistent with the values of Z derived from life history parameters and other analyses. This is exemplified by the mortality estimates derived for Acanthopagrus latus in a large marine embayment. Thus, such estimates, calculated for M for this population from a growth parameter and from growth parameters and water temperature, were both 0.70·year1, whereas those for Z, calculated from maximum recorded age, relative abundance analysis, and a simulation based on maximum age and sample size, ranged from 0.18 to 0.30·year1. These results are clearly inconsistent. A Bayesian approach was therefore developed that combines the posterior probability distributions of the various mortality estimates and thereby produces integrated and consistent estimates for M and Z. The application of our Bayesian approach to the data for A. latus yielded lower values for M than for Z. Our approach is equally applicable to other fish species.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".