Living the high turbidity life: The effects of total suspended solids, flow, and gill morphology on mussel feeding
Bibliographic record
Abstract
Abstract Unionid mussels from clear‐water rivers are often found in turbid waters in which their feeding and reproductive efficiency should be impaired. We examined the feeding response of Lampsilis siliquoidea from a clear and turbid river in response to increased concentrations of total suspended solids (TSS) and water velocity in a funnel chamber and a recirculating flow chamber. Four TSS concentrations (0, 5, 20, and 100 mg L −1 ) and four velocities (0, 0.05, 0.15, and 0.25 m s −1 ) were used to create 16 experimental conditions corresponding to nine TSS flux (concentration × velocity) levels. TSS flux significantly affected clearance rates (CR); however, the relationship was complex. Increased TSS led to lower CR in mussels from clear and turbid river at all velocities; however, increases in velocity reduced the magnitude of the decline with respect to TSS. Overall, the turbid river mussels were less affected by increased TSS than those from the clear‐water rivers. The mussels from the turbid river also differed anatomically (i.e., greater palp:gill ratio; number of cilia per cirri; number of cirri cm −1 ; and thickness of palp cilia, and less cilia per unit area of palp) from the clear river animals. It is likely that these anatomical differences allow the mussels to feed more efficiently in turbid water, which explains the observed differences in CR response to TSS. These results demonstrate the importance of multistressor approaches in evaluating aquatic organisms, as well as providing a potential mechanism to explain how mussels thrive in turbid rivers.
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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".