The effect of settling velocity on the transport of mussel larvae in a cobble‐bed river: Water column and near‐bed turbulence
Bibliographic record
Abstract
Lay Abstract Simple transport models predict that the distance small organisms such as larvae “drift” downstream in rivers is determined by their settling velocity, the release height, and the stream velocity. However, natural turbulent conditions in a river may also affect the downstream transport and dispersion (spread) of larvae. The main goal of this study was to examine how stream velocity and larval settling velocity (Mucket [ Actinonaias ligamentina ] and the Wavy‐rayed Lampmussel [ Lampsilis fasciola ] differ by 2.5 times) affect the transport of freshwater unionid mussel larvae in the Grand River, Ontario, Canada. Larvae were released and captured in a series of nets downstream. Larval spread in rivers appeared to be strongly affected by stream flow conditions. Larvae were spread more rapidly with increased stream velocity likely due to increased turbulence in the water. Overall there was a good agreement between measured downstream decrease in capture of larvae and predictions from a 3‐dimensional advection–diffusion model that considered the spread due to hydrodynamics. However, in contrast to the predictions of simple transport models, differences in settling velocity had no detectable effect on the transport of larvae. Future studies are necessary to further explore the role of settling velocity and other factors under different stream flow conditions, which may also be important for dispersal of other organisms and particles.
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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".