Size matters: Effects of propagule size on dispersal in rivers
Bibliographic record
Abstract
Biological particles (e.g., bacteria, eggs, fruit/seeds, and larvae) of a wide range of sizes (i.e., 10-6 – 10-1 m) are transported over various distances (i.e., 100 - 104 m) downstream in rivers. We examined the effects of propagule size on downstream dispersal by releasing biodegradable microbeads (density ~ 1200 kg m-3) of three size classes (~150, 250, 350 µm) at the Speed River, Guelph, ON. Hitting distance estimates and longitudinal dispersion coefficients declined with particle size and were significantly different between 150 and 350 µm microbeads. The magnitude of these differences was relatively small (~ 5 m) because of the slow velocity (9.5 ± 0.01 cm s-1) and low turbulence (shear velocity = 1.9 ± 0.13 cm s-1) in the river. We examined the dispersion of larval and juvenile unionid mussels (size range = 56 – 415 µm, 247.54 ± 60.38 [mean ± SD] µm, N = 174) across a broader range of flow conditions by applying laminar and turbulent flow models in three river reaches of increasing velocity and turbulence. Model results indicated that the dispersal of smaller larvae increased disproportionately with increasing water column turbulence. Given that the peak in the size frequency distribution of larvae and juveniles corresponded to Rouse numbers (ratio of gravitational settling to water column turbulence) P < 1, we suggest a trade-off in propagule size in the taxon, whereby the increased dispersal of smaller juveniles may come at the cost of reduced settlement success in turbulent environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".