Effects of acute suspended sediment exposure on the swimming and schooling performance of imperilled Redside Dace (<i>Clinostomus elongatus</i>)
Bibliographic record
Abstract
Urbanization is a widespread threat to freshwater ecosystems. After rainfall, urban streams often experience unnaturally fast water flows and acute increases in suspended sediment due to the high degree of adjacent impervious land surface. Suspended sediments may negatively affect fishes by impairing respiration, and reduced water clarity may also affect social behaviours such as schooling that are dependent on visual cues. Given these two mechanisms of harm, suspended sediments may therefore exacerbate the difficulty of swimming at high water velocities. We tested this idea using imperilled Redside Dace ( Clinostomus elongatus) to examine the consequences of suspended sediment on swimming performance and schooling behaviour. Using individual fish, we assayed swimming performance (standard critical swim speed test) and tail beat frequency and amplitude under a range of ecologically relevant sediment concentrations. Next, we measured the impact of sediment on the cohesion and polarization of schools. Swimming performance of individual fish was not affected by suspended sediment levels we examined. School polarization was positively correlated with water flow overall and at the fastest flows we tested; schools were more polarized when exposed to sediment. School cohesion decreased with increasing flows and was unaffected by the suspended sediment levels we examined. Our results collectively suggest that swimming performance of Redside Dace may be resilient to ecologically relevant acute suspended sediment exposure.
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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".