Fish predation and trapping for rusty crayfish (<i>Orconectes rusticus</i>) control: a whole-lake experiment
Bibliographic record
Abstract
Improved methods are needed for the prevention and control of invasive species. We investigated the potential to control a rusty crayfish (Orconectes rusticus) population in an isolated lake in northern Wisconsin by trapping adult crayfish and restricting fishing, thereby increasing fish populations and predation on small crayfish. Over a 3 year period, traps and predatory fishes removed substantial portions of the rusty crayfish population. We used an age-structured population model to determine which removal method had the largest effect on crayfish population growth rates. Because more crayfish were vulnerable to and removed by fish predation than by trapping, fish predation caused a larger decline in the population growth rate. However, trapping removed crayfish with the highest reproductive value and caused the largest decline in population growth rate per individual crayfish removed. Consideration of density-dependent responses to removal is necessary to predict long-term effects on rusty crayfish population dynamics. Nonetheless, our results suggest that the combination of trapping and fish predation can control established rusty crayfish populations and deserves further consideration for management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".