Comparative distribution and invasion risk of snakehead (Channidae) and Asian carp (Cyprinidae) species in North America
Bibliographic record
Abstract
As nonindigenous species are a major threat to global biodiversity, cost-effective management requires identification of areas at high risk of establishment. Here we predict suitable environments of 14 high-profile species of nonindigenous snakehead (Channidae) and Asian carp (Cyprinidae) species in North America based upon ecological niche modelling and compare the driving environmental variables for the two fish groups. Snakeheads distributions were correlated with thermal factors, whereas those of Asian carps were related mainly to precipitation. Predicted suitable ranges for these nonindigenous species can be divided into three main areas: Mexico and the southern United States (five species); Mexico and the United States up to ~35 °N (three species); and most of Mexico, continuous United States, and southern Canada (six species). For the province of Ontario, we combined the number and location of aquarium stores and live fish markets with predicted areas of suitable environments to identify areas at risk of introduction and establishment. We identified several watersheds draining into northwestern Lake Ontario as having the highest risk, highlighting the increased predictive value of this approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".