Introducing the symposium "Building on Beverton's legacy: life history variation and fisheries management"
Bibliographic record
Abstract
Throughout his career, Ray Beverton displayed an interest in the life history diversity in marine and freshwater fish. The papers collected here describe recent research directed at documenting this diversity and understanding both its consequences and the processes that generate it. There are three themes: factors that direct life history dynamics; fishing as a force that redirects life history dynamics; and roles for life history statics in conservation management. The "dynamics" papers show that fish life histories can evolve in response to both natural and harvest-induced selective pressures. Evolution in response to harvesting can be rapid, with potentially dramatic effects on population dynamics and sustainable exploitation. The "statics" articles demonstrate how maturity traits combine with shifts in habitat use to shape the sensitivity of a population to habitat loss. Life history shifts can dramatically alter the safety of harvesting policies that were prudent in the past; shifts of the predators or prey of a harvested species can be as important as shifts in the harvested species itself. Further work on the ecological circumstances that favour different degrees of plastic or genetic life history responses to human impacts are needed to prevent inadvertent induction of long-lasing evolutionary changes in fish life histories.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".