Differences in tolerance to and recovery from zebra mussel (<i>Dreissena polymorpha</i>) fouling by <i>Elliptio complanata </i>and <i>Lampsilis radiata</i>
Bibliographic record
Abstract
Zebra mussels (Dreissena polymorpha) in Lake Champlain have colonized the shells of many native unionids, causing declines in their abundance. Periodically cleaning zebra mussels from unionids may be an effective conservation technique, if unionids can recover from the stress induced by zebra mussels. Efforts will need to target species that are most vulnerable to fouling and subsequent energetic losses. We used glycogen as a biochemical indicator of energetic stores to assess species-specific differences in tolerance to zebra mussels. There was no evidence that glycogen levels decreased as dreissenid/unionid mass ratios increased in Elliptio complanata. However, dreissenid/unionid mass ratios as low as 0.25 in Lampsilis radiata were correlated with a significant decline in glycogen content. The ability of these species to recover glycogen after zebra mussel removal and replacement in situ was also evaluated. Mussels were cleaned of zebra mussels and replaced in situ. After 10 weeks, cleaned, heavily fouled, and never-fouled (control) mussels were collected. Glycogen levels in fouled mussels were lower than in the control mussels, while glycogen levels in cleaned mussels did not differ from the control mussels. Results suggest that heavily fouled E. complanata and L. radiata can recover glycogen levels if cleaned of zebra mussels and that cleaning may be a viable option for unionid conservation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".