Propagules go with the flow: Near‐field particle dispersion in reaches with different hydrodynamic conditions
Bibliographic record
Abstract
Abstract We examined the effects of riverbed roughness and turbulence (shear velocity, ) on propagule dispersion in the near‐field region (< 100 m) by releasing microbead models of larval and juvenile unionid mussels in tributaries of the Grand River (Ontario, Canada). The Conestogo River had the roughest bed and highest mean , followed by the Grand and Speed rivers. We predicted more downstream transport with higher velocities and that longitudinal dispersion coefficients (Kx) would match the patterns in roughness and . The Conestogo River had the highest downstream particle flux and Kx as predicted by simple empirical equations. Inconsistent with model predictions, however, the Grand River had the lowest particle flux and Kx. These differences were greater than expected based on the small differences in reach‐averaged mean velocities between the Grand and Conestogo rivers. This mismatch between Kx predicted by simple empirical models and those fit to an advection–diffusion model was related to the inertial properties of the flow in the advective zone (i.e., near field) of the reaches. Streamwise, lateral, and especially vertical velocities at drift nets were spatially heterogeneous within and among reaches, demonstrating the influence of the flow direction on particle flux. Although bulk fluid statistics provided a useful indication of how dispersal distances vary among rivers, our results suggest that near‐field dynamics can be complex, requiring high‐resolution bathymetry and velocity data for the development of improved advection–diffusion models. Care should be exercised in predicting the dispersal of particles at ecologically relevant spatial scales in 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 | 0.001 |
| 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".