Observations of particle capture on a cylindrical collector: Implications for particle accumulation and removal in aquatic systems
Bibliographic record
Abstract
Capture of suspended particles by cylindrical collectors is an important mechanism in many aquatic processes, such as larval settlement, suspension feeding, and vegetative filtration. In these processes, the collector Reynolds number (Rec), based on the collector diameter, ranges from well below 1 to 1,000. No analytical solutions exist to describe capture over most of this range. Capture is typically described by the efficiency, η, defined as the ratio of the upstream span of particles that are captured on the collector to the collector diameter. Here, laboratory experiments are used to measure capture efficiency of a single cylinder as a function of Rec and particle ratio, R, which is the ratio of particle diameter to collector diameter. Rec is varied from 50 to 500 and three values of R are used: 0.03, 0.015, and 0.008. The selected particles have a specific gravity of 1.03. For smooth cylinders, capture increases with both Rec and R but is more strongly dependent on R. This result indicates that, in aquatic systems, where flow velocity and suspended particle type and size are fixed, higher capture efficiency will occur on the smallest collectors (those with largest R). Furthermore, we examine a similar experiment in which particles are collected by branched structures. We show that capture to individual cylindrical branches within a compound structure can be predicted by single‐cylinder efficiencies. Finally, capture was increased when roughness elements were added to the collectors.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".