Bayesian fishable biomass dynamics models incorporating fished area and relative fish density
Bibliographic record
Abstract
Fisheries typically experience large changes over time in fishing effort. The size of the area fished may also change substantially over time, mimicking the trend in fishing effort, and may have major effects on the population dynamics and fishing process. We extend a biomass dynamics model to incorporate fished area and relative fish density in fished and unfished areas. The fishable population is defined as those individuals in the fished area and those that are sufficiently close to the fished area that they could potentially move into fished area during the fishing season. We estimate fishable biomass using three models assuming different level of population mixing between fished and unfished areas (i.e., partial mixing, full mixing, and no mixing). The models are implemented within a hierarchical Bayesian framework. Model performance is explored using simulations, and the approach is illustrated using logbook data for two tiger prawn species in Australia’s Northern Prawn Fishery. The partial mixing model that involves estimating a mixing parameter performs better than the models that assume no or full mixing. The methods could be applied to other fisheries where the area fished has changed substantially over the history of the fishery.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".