Novel muscle and connective tissue design enables high extensibility and controls engulfment volume in lunge-feeding rorqual whales
Bibliographic record
Abstract
Muscle serves a wide variety of mechanical functions during animal feeding and locomotion, but the performance of this tissue is limited by how far it can be extended. In rorqual whales, feeding and locomotion are integrated in a dynamic process called lunge feeding, where an enormous volume of prey-laden water is engulfed into a capacious ventral oropharyngeal cavity that is bounded superficially by skeletal muscle and ventral groove blubber (VGB). The great expansion of the cavity wall presents a mechanical challenge for the physiological limits of skeletal muscle, yet its role is considered fundamental in controlling the flux of water into the mouth. Our analyses of the functional properties and mechanical behaviour of VGB muscles revealed a crimped microstructure in an unstrained, non-feeding state that is arranged in parallel with dense and straight elastin fibres. This allows the muscles to accommodate large tissue deformations of the VGB yet still operate within the known strain limits of vertebrate skeletal muscle. VGB transverse strains in routine-feeding rorquals were substantially less than those observed in dead ones, where decomposition gas stretched the VGB to its elastic limit, evidence supporting the idea that eccentric muscle contraction modulates the rate of expansion and ultimate size of the ventral cavity during engulfment.
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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".