Evaluating recreational fisheries for an endangered species: a case study of taimen, Hucho taimen, in Mongolia
Bibliographic record
Abstract
Understanding the tradeoff between exploitation and conservation is difficult in data-poor situations, which are typical for most recreational fisheries, even in developed countries. In a developing country where the target species is endangered, the stakes are higher and the management resources are fewer. We combined a mark–recapture experiment, life history invariants, and meta-analysis to parameterize a delay-difference model for a population of the endangered giant Eurasian trout (taimen, Hucho taimen ) in northern Mongolia. The model allowed us to evaluate the impacts of a recreational fishery for taimen based on a suite of population characteristics including equilibrium abundance, biomass, and mean weight. The Bayesian framework and Monte Carlo simulations combine disparate sources of information while keeping track of uncertainty as it propagates through the model. In the case of taimen in the Eg–Uur watershed, the existing catch–release recreational fishery has likely reduced taimen abundance, biomass, and mean weight by less than 10% compared with levels predicted in the absence of recreational fishing. In comparison, if all taimen caught in this fishery were retained (as they are elsewhere in Mongolia), there is a 57% chance that such harvest levels, if maintained, would lead to the eventual extirpation of the population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".