Influence of physicochemical factors on the distribution and biomass of invasive mussels (<i>Dreissena polymorpha</i> and <i>Dreissena bugensis</i>) in the St. Lawrence River
Bibliographic record
Abstract
Twenty sites along the St. Lawrence River were sampled to determine if the distribution and abundance of invasive mussels (zebra mussel (Dreissena polymorpha) and quagga mussel (Dreissena bugensis)) are explained by physicochemical variables. Calcium concentration, substrate size, and depth independently explained significant proportions of variation in biomass for both species. Zebra mussel populations occurred at calcium levels as low as 8 mg Ca·L 1 , but quagga mussels were absent below 12 mg Ca·L 1 , suggesting that they have higher calcium requirements. Both species increased in biomass with increasing substrate size but displayed contrasting patterns with depth. Using combinations of these environmental variables, we developed stepwise multiple regression models to predict zebra mussel biomass and quagga mussel biomass. The zebra mussel model included calcium concentration, substrate size, and depth (r 2 = 0.36, P < 0.0001), while the quagga mussel model included only substrate size and depth (r 2 = 0.32, P < 0.0001). These results suggest that dreissenid mussel abundance (and correlated impacts) will vary predictably across environmental gradients, but the same predictive model will not be accurate for both species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".