Potentially high-risk freshwater invasive species in Quebec: a screening-level risk assessment of 46 non-indigenous species
Bibliographic record
Abstract
As ecological and economic impacts of non-indigenous species (NIS) are a growing concern globally, screening-level risk assessments can help to rapidly prioritize the allocation of resources.A screening-level risk assessment was performed using the Canadian Marine Invasive Screening Tool (CMIST) to assess the potential ecological risks posed by 46 non-indigenous species (fish, molluscs, crustaceans, plants and algae) in 6 areas of Quebec.Out of 46 NIS assessed, 25 were identified as high-risk in at least one area of the province.Some species, such as Goldfish (Carassius auratus), Eurasian watermilfoil (Myriophyllum spicatum), waterthyme (Hydrilla verticillata), and curly-leaf pondweed (Potamogeton crispus) appear to be high risk in most areas of Quebec.However, risk scores (and numbers of potentially high-risk species) were greater in the southern areas of the province, particularly adjacent to the St. Lawrence River, and lowest in the most northerly regions.Using the CMIST scores, a list of the species posing the greatest ecological risk was developed.This included several well-known invasive species, such as dreissenid mussels (Dreissena polymorpha and D. bugensis) and several plants including M. spicatum and P. crispus.A disproportionately large number of the "high risk" species were plants.High risk species should be priority targets for future assessments and potential management actions.More investment in control, management, mitigation and research concerning aquatic invasive plants in Canadian waters is called for.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".