A perfectly inelastic collision: Bulk prey engulfment by baleen whales and dynamical implications for the world's largest cetaceans
Bibliographic record
Abstract
The largest animals are the rorquals, a group of whales which rapidly engulf large aggregations of small-bodied animals along with the water in which they are embedded, with the latter subsequently expulsed via filtration through baleen. Represented by species like the blue, fin, and humpback whales, rorquals can exist in a wide range of body lengths (8–30 m) and masses (4000–190,000 kg). When feeding on krill, kinematic data collected by whale-borne biologging sensors suggest that they first oscillate their flukes several times to accelerate towards their prey, followed by a coasting period with mouth agape as the prey-water mixture is engulfed in a process approximating a perfectly inelastic collision. These kinematic data, used along with momentum conservation and time-averages of a whale's equation of motion, show the largest rorquals as generating significant body forces (10–40 kN) in order to set into forward motion enough engulfed water to at least double overall mass. Interestingly, a scaling analysis of these equations suggests significant reductions in the amount of body force generated per kilogram of body mass at the larger sizes. In other words, and in concert with the allometric growth of the buccal cavity, gigantism would involve smaller fractions of muscle mass to engulf greater volumes of water and prey, thereby imparting a greater efficiency to this unique feeding strategy.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".