Cardiovascular design in fin whales: high-stiffness arteries protect against adverse pressure gradients at depth
Bibliographic record
Abstract
Fin whales have an incompliant aorta, which, we hypothesize, represents an adaptation to large, depth-induced variations in arterial transmural pressures. We hypothesize these variations arise from a limited ability of tissues to respond to rapid changes in ambient ocean pressures during a dive. We tested this hypothesis by measuring arterial mechanics experimentally and modelling arterial transmural pressures mathematically. The mechanical properties of mammalian arteries reflect the physiological loads they experience, so we examined a wide range of fin whale arteries. All arteries had abundant adventitial collagen that was usually recruited at very low stretches and inflation pressures (2-3 kPa), making arterial diameter largely independent of transmural pressure. Arteries withstood significant negative transmural pressures (-7 to -50 kPa) before collapsing. Collapse was resisted by recruitment of adventitial collagen at very low stretches. These findings are compatible with the hypothesis of depth-induced variation of arterial transmural pressure. Because transmural pressures depend on thoracic pressures, we modelled the thorax of a diving fin whale to assess the likelihood of significant variation in transmural pressures. The model predicted that deformation of the thorax body wall and diaphragm could not always equalize thoracic and ambient pressures because of asymmetrical conditions on dive descent and ascent. Redistribution of blood could partially compensate for asymmetrical conditions, but inertial and viscoelastic lag necessarily limits tissue response rates. Without pressure equilibrium, particularly when ambient pressures change rapidly, internal pressure gradients will develop and expose arteries to transient pressure fluctuations, but with minimal hemodynamic consequence due to their low compliance.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".