Floating point: a computational study of buoyancy, equilibrium, and gastroliths in plesiosaurs
Bibliographic record
Abstract
Three-dimensional mathematical/computational models of three types of plesiosaur (Liopleurodon - short neck, Cryptoclidus - medium neck, and Thalassomedon - long neck) were used to investigate aspects of their flotation and stability. Equivalent models of an extant alligator (Alligator mississippiensis) and leatherback sea turtle (Dermochelys coriacea) were used as tests. With full lungs, and uniform tissue densities of 1,050 g/l, all five models would float at the surface, with the alligator and sea turtle models replicating the depths of immersion and inclinations observed in living forms. Impractically large amounts of gastroliths were needed to initiate sinking - even with the lungs 50% inflated,10 kg of stones were still required in a 218 kg Cryptoclidus to produce negative buoyancy, and the hypothesis that gastroliths were for control of buoyancy is rejected. However, gastroliths equal to 1% of body weight in the Thalassomedon model were effective at damping out buoyant oscillations of the neck when at the surface and minimizing instability when fully immersed at 10 meters depth. The oblate bodies of Cryptoclidus and Liopleurodon provided effective passive mechanisms for righting the body if perturbed by waves at the surface, but the almost circular cross-section of the Thalassomedon body was ineffective in self-righting. The relatively longer flippers of Thalassomedon may have provided higher drag to resist rolling. The idea that plesiosaurs could maintain their necks above the water surface in an erect manner is rejected due to unbalanced buoyancy torques acting on the body.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".