Oral cavity hydrodynamics and drag production in Balaenid whale suspension feeding
Bibliographic record
Abstract
Balaenid whales feed on large aggregates of small and slow-moving prey (predominantly copepods) through a filtration process enabled by baleen. These whales exhibit continuous filtration, namely, with the mouth kept partially opened and the baleen exposed to oncoming prey-laden waters while fluking. The process is an example of crossflow filtration (CFF) in which most of the particulates (prey) are separated from the substrate (water) without ever coming into contact with the filtering surface (baleen). This paper discusses the simulation of baleen filtration hydrodynamics based on a type of hydraulic circuit modeling commonly used in microfluidics, but adapted to the much higher Reynolds number flows typical of whale hydrodynamics. This so-called Baleen Hydraulic Circuit (BHC) model uses as input the basic characteristics of the flows moving through a section of baleen observed in a previous flume study by the authors. The model has low-spatial resolution but incorporates the effects of fluid viscosity, which doubles or more a whale's total body drag in comparison to non-feeding travel. Modeling viscous friction is crucial here since exposing the baleen system to the open ocean ends up tripling a whale's total wetted surface area. Among other findings, the BHC shows how CFF is enhanced by a large filtration surface and hence large body size; how it is carried out via the establishment of rapid anteroposterior flows transporting most of the prey-water slurry towards the oropharyngeal wall; how slower intra-baleen flows manage to transfer most of the substrate out of the mouth, all the while contributing only a fraction to overall oral cavity drag; and how these anteroposterior and intra-baleen flows lose speed as they approach the oropharyngeal wall.
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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".