A spatial model for fishery age-selection at the population level
Bibliographic record
Abstract
Different age classes do not generally experience the same rates of fishing mortality. The processes resulting in age- (or length-) selection operate at several scales. At the broadest scale, population-selection measures the age-specific probability of capture, while at the finest scale contact-selection describes the vulnerability of fish that encounter the fishing gear. Population-selectivity is the process most relevant to fish population dynamics and stock assessment, but it has received far less attention than processes operating at gear-specific scales. Despite wide recognition of the diverse shapes possible for population-selectivity, the processes determining these shapes are poorly understood. This paper develops a reasonably simple model of population-selectivity from a set of survival equations, coupled to allow movement between subpopulations, and explores the conditions necessary to produce different shaped population-selection curves. Important factors influencing the population-selectivity model are the gear-specific selection characteristics of the fleets, their effort levels relative to one another, the spatial distribution of fishing mortality, and the movement of fish between subpopulations. The model can generate quite complicated curves and has surprising properties. For example, under a wide variety of conditions, even though the same asymptotic gear-selectivity applies in all subpopulations, the overall population-selectivity will be dome-shaped unless fishing mortality is uniform across all subpopulations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".