The capture of crude oil droplets by filter feeders at high and low Reynolds numbers
Bibliographic record
Abstract
Crustacean filter feeders capture oil droplets with the use of their ramified appendages. These appendages behave as paddles or sieves, based on the system's Reynolds number. Here, we used high-speed videography, scanning electron microscopy and fluid mechanics to study the capturing mechanisms of crude oil droplets and the filtering appendage's wettability by two species of barnacles (Balanus glandula and Balanus crenatus) and of the freshwater cladoceran Daphnia magna. Our results show that barnacle appendages behave as paddles and capture droplets in their boundary layers at low Reynolds number. At high Reynolds number, droplets are most likely to be captured via direct interception. There is an intermediate range of Reynolds number where droplets can be captured by both mechanisms at the same time. Daphnia magna captures droplets in the boundary layers of the third and fourth pair of thoracic legs with a metachronal motion of the appendages. All studied surfaces were revealed to be highly lipophobic, demonstrating captured oil droplets with high contact angles. We also discuss implications of such capture mechanisms and wettability on potential ingestion of crude oil by filter feeders. These results further our understanding of the capture of crude oil by filter feeders, shedding light on the main entry point of oil in marine food webs.
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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".