Influence of physico-chemical factors on the distribution and biomass of invasive mussels in the St. Lawrence River
Bibliographic record
Abstract
Biological invasions threaten the stability and biodiversity of freshwater ecosystems worldwide. The impacts of an invading species often vary across systems, making their prediction difficult. When data from multiple invaded sites are available, statistical models can be developed to correlate an invader's distribution and abundance with local environmental variables; such models could then provide managers with useful tools to help prioritize efforts to control the invader. The introduction of the zebra mussel (Dreissena polymorpha) and quagga mussel (D. bugensis) to North America ranks among the most ecologically and economically disruptive aquatic invasions ever documented. While some attempts have been made to predict zebra mussel occurrence and abundance, none have been made for quagga mussels. Furthermore, few studies have been based on river systems, which possess the bulk of North American freshwater biodiversity. I related zebra and quagga mussel occurrence and biomass to physical habitat variables (calcium concentration, substrate size and depth) in the St. Lawrence River. I then developed predictive models of abundance for each species from combinations of these variables. Each variable explained a significant amount of variation in mussel biomass, but different combinations of variables were obtained for each species. Although these models do not account for all of the variation in abundance, they do provide a useful basis for predicting dreissenid distribution and abundance in other invaded river systems.
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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 | 0.000 |
| 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 | 0.000 | 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".