Predicting zebra mussel fouling on native mussels from physicochemical variables
Bibliographic record
Abstract
Summary 1. Predictive models of impact are needed for the risk assessment of invasive species. One such species is the Eurasian zebra mussel (Dreissena polymorpha), a fouling bivalve that overgrows and kills native mussels (Unionidae) in many North American lakes and rivers. The level of mortality in a native mussel population increases with the fouling intensity – i.e. the mean number of zebra mussels attached to each native mussel. 2. We conducted a multi‐site survey within the St Lawrence and Richelieu rivers (Quebec, Canada) to determine whether zebra mussel fouling intensity can be predicted from environmental variables. We found fouling intensity to be positively correlated with calcium concentration [Ca2+] and negatively correlated with sediment size, but not affected by local macrophyte cover. 3. A multiple regression model that includes calcium concentration and sediment size explains 86% of variation in fouling intensity across all sites. 4. Analysis of data from invaded sites in North America and Europe revealed a nonlinear relationship in which fouling intensity increases with calcium concentration up to an asymptotic threshold of 24 mg L−1 Ca2+. 5. Our results suggest that the community‐level impacts of zebra mussels are mediated by abiotic environmental variables and gradients in these variables may provide local habitat refugia for native mussels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".