Morphology and Mechanics of the Fin Whale Esophagus: The Key to Fast Processing of Large Food Volumes by Rorquals
Bibliographic record
Abstract
Lunge feeding rorqual whales feed by engulfing a volume of prey laden water that can be as large as their own body. Multiple feeding lunges occur during a single foraging dive and the time between each lunge can be as short as 30 s (Goldbogen et al. 2013). During this short inter-lunge time, water is filtered out through baleen to concentrate prey in the oral cavity, and then the prey is swallowed prior to initiating the next lunge. Prey density in the ocean varies greatly, and despite the potential of swallowing a massive volume of concentrated prey as a slurry, the esophagus of rorqual whales has been anecdotally described as unexpectedly narrow with a limited capacity to expand. How rorquals swallow large quantities of food down a narrow esophagus during a limited inter-lunge time remains unknown. Here, we show that the small diameter muscular esophagus in the fin whale is optimized to transport a slurry of food to the stomach. A thick wall of striated muscle occurs at the pharyngeal end of the esophagus which, together with the muscular wall of the pharynx, may generate a pressure head for transporting the food down the esophagus to the stomach as a continuous stream rather than separating the food into individual boluses swallowed separately. This simple model is consistent with estimates of prey density and stomach capacity. Rorquals may be the only animals that capture a volume of food too large to swallow as a single intact bolus without oral processing, so the adaptations of the esophagus are imperative for transporting these large volumes of concentrated food to the stomach during a time-limited dive involving multiple lunges.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".