March of the Mussel: Is Atlantic Canada at risk of zebra mussel ( <i>Dreissena polymorpha</i> ) invasion?
Bibliographic record
Abstract
The invasion of Atlantic Canadian (Eastern Quebec, New Brunswick, Nova Scotia, and Prince Edward Island) freshwater habitats by the zebra mussel ( Dreissena polymorpha) is currently in progress. Management of this ongoing invasion requires information on which areas have the highest invasion risk, which can be predicted using species distribution models (SDMs). Previous Canadian studies have focused on environmental suitability, with less use of information on potential pathways of introduction, and have tended to rely on single SDMs. In this study, the risk of Dreissena polymorpha spread in Atlantic Canada was explored using multiple SDM types, both separately and in combination via two ensemble modelling approaches, noting which approach increased SDM accuracy. Models were built using physico-chemical data coupled with spatially explicit information on human-mediated introduction pathways. Physico-chemical variables, and data relating to recreational fishing, were among the more important model variables. Model outputs identified several “higher-risk” areas for Dreissena polymorpha invasion that could be prioritized for future monitoring and management action. Of the trialed model types, random forest models were the most accurate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".