Geometric analysis of airway trees shows that lung anatomy evolved to enable explosive ventilation and prevent barotrauma in cetaceans
Bibliographic record
Abstract
ABSTRACT Two new biomechanical challenges faced cetacean lungs compared to their terrestrial ancestors. First, hydrostatic pressures encountered during deep dives are sufficient to cause nearly full lung collapse, risking substantial barotrauma during surfacing if air is trapped in the fragile smaller airways. Second, rapid ventilation in large cetaceans requires correspondingly high ventilatory flow rates. In order to investigate how airway geometry evolved in response to these challenges, we characterized airway geometry from 12 species of cetaceans that vary in common dive depth and ventilatory behavior and a domestic pig using computed tomography. After segmenting the major airways, we generated centerline networks models for the larger airways and computed geometric parameters for each tree including mean branching angle, percent volume fraction, and Strahler branching, diameter, and length ratios. When airway geometry was regressed against ventilatory and diving parameters with phylogenetic least squares, neither average branching angle, percent volume fraction, Strahler length ratio or Strahler branching ratio significantly varied with common ventilatory mode or common diving depth. Higher Strahler diameter ratios were associated with slower ventilation and deeper diving depth, suggesting that cetacean lungs have responded to biomechanical pressures primarily with changes in airway diameter. High Strahler diameter ratios lungs in deeper diving species may help to facilitate more complete collapse of the delicate terminal airways by providing for a greater incompressible volume for air storage at depth. On the other hand, lungs with low Strahler diameter ratios would be better for fast ventilation because the gradual decrease in diameter moving distally should keep peripheral flow resistance low, maximizing ventilatory flow rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".