Risk analysis for species introductions: forecasting population growth of Eurasian ruffe (<i>Gymnocephalus cernuus</i>)
Bibliographic record
Abstract
The North American distribution of the Eurasian ruffe (Gymnocephalus cernuus), an ecologically important and costly invasive fish, is presently limited to the Laurentian Great Lakes. Risk analyses for accidental introductions of ruffe to inland lakes should focus on the chance of establishment for small introductions such as those that would result from transporting ruffe as bait. Here I use Akaike's Information Criterion to select a population growth model for ruffe based on observed population dynamics during the invasion of Loch Lomond, Scotland. This population is regulated by a high carrying capacity and Allee effects were undetected. Parameter estimates obtained from this population forecast that the chance of establishment for possible introductions of ruffe to inland lakes in North America is high. A model for ruffe winter survival suggests that survivorship between introductions and spawning may be an important determinant of establishment success, but that the chance of establishment is high for introductions of only a few individuals. To prevent invasions of ruffe in inland waters, release of ruffe, whether intentional or accidental, should not be tolerated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".