National aquatic invasive species (AIS) risk assessment for Zebra Mussel (Dreissena polymorpha) and Quagga Mussel (Dreissena rostriformis bugensis), April 2022
Bibliographic record
Abstract
Zebra Mussel (Dreissena polymorpha) and Quagga Mussel (D. rostriformis bugensis) are aquatic invaders with substantial economic and ecological impacts that continue to spread in Canada. A new ecological risk assessment (that differs from the 2012 assessment) was conducted by Fisheries and Oceans Canada (DFO) Science for freshwater ecosystems across Canada incorporating updated and improved data with greater resolution (9,260 × 9,260 m grid cell). This risk assessment characterized the potential for mussels to be introduced (propagule pressure) and establish (habitat suitability), along with their potential ecological impacts to derive a metric of Ecological Risk for two separate scenarios of establishment using either a calcium-based model or a maximum entropy (MaxEnt) habitat suitability model. Ecological Risk values are not absolute, and areas of Low risk do not necessarily indicate that Zebra and Quagga Mussel cannot be introduced, establish, or impact those Canadian ecosystems, but rather indicates that the risks are lower relative to areas at higher risk. As such, both scenarios identified Low to High risk areas with sub-drainages with the highest risk in proximity to the current distribution of these species, particularly the Laurentian Great Lakes system (both Zebra Mussel and Quagga Mussel) and Manitoba (Zebra Mussel). Outside of the current distribution, calcium-based models for both species identified Moderate risk areas throughout the southern portions of most provinces. In the Maritime provinces for which data was not available in the previous assessment, most habitat suitability models identified the area as Moderate risk for both Quagga and Zebra Mussel, particularly New Brunswick which also exhibited some discrete areas of High risk. For the rest of Canada, including Newfoundland and Labrador and the Territories for which data were also unavailable in the previous assessment, the risk for both species across most habitat suitability models was predominantly Low, with most of the Arctic Archipelago being below the thermal tolerance for both species. To facilitate Aquatic Invasive Species (AIS) management decision-making, Ecological Risk is summarized at the sub drainage level for all of Canada which was the spatial scale used in the 2012 assessment.
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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 | 0.001 |
| 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".