The hidden risk of keystone invaders in Canada: a case study using nonindigenous crayfish
Bibliographic record
Abstract
Invasive species have long been recognized as a serious threat to freshwater ecosystems. This is especially true for invasive species in keystone positions in food webs that can cause major disruption and can lead to unexpected outcomes. Crayfish occupy a central trophic position and nonindigenous crayfish have been shown to substantially disrupt ecosystems they invade. Here, we assess eight nonindigenous crayfish to 21 freshwater ecoregions in Canada using a screening-level risk assessment. We found that ecoregions in Canada that were warmer and contained high native freshwater diversity were most at risk from crayfish invasions, particularly: the Laurentian Great Lakes, St. Lawrence, English–Winnipeg lakes, and Coastal British Columbia ecoregions. Four crayfish species consistently had higher-risk scores: rusty ( Faxonius rusticus), virile ( Faxonius virilis), signal ( Pacifastacus leniusculus), and red swamp ( Procambarus clarkii). Of these high-risk crayfish, only the red swamp crayfish is not yet established in Canada but is present in US waters of the transboundary Great Lakes ecoregion. Our study is the first to evaluate the relative risks that nonindigenous crayfish pose to freshwater ecosystems in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".