The Soft Palate Enables Extreme Feeding and Explosive Breathing in the Fin Whale (<i>Balaenoptera physalus</i>)
Bibliographic record
Abstract
The evolution of lunge feeding in rorqual whales was associated with the evolution of several unique morphological features that include non-synovial ligamentous temporomandibular joints, a tongue that can invert and extend backward to the umbilicus, walls of the oral cavity that can dramatically expand, and muscles and nerves that are stretchy. Also, among the acquired features was an enlargement of the rostral end of the soft palate into an oral plug that occludes the opening between the oral cavity and pharynx and prevents water incursion into the pharynx during the engulfment phase of a feeding lunge. During this engulfment phase of a lunge, the volume of water entering the oral cavity can exceed the volume of the whale itself. Here, using dissection of fetuses and adults and a magnetic resonance imaging dataset of a fetus, we examine the detailed anatomy of the soft palate in fin whales. We describe several innovative features relative to other mammals, including changes in the attachment and positions of the major extrinsic muscles of the palate, alterations in the morphology of the pterygoid processes related to the palate and pharynx, and the presence of distinct muscle layers in the part of the palate caudal to the oral plug. Based on the anatomy, we present a model for how the soft palate is positioned at rest, and how it functions during feeding, breathing, and swallowing.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".