Swimming speed estimation of extinct marine reptiles: energetic approach revisited
Bibliographic record
Abstract
Cruising speeds of Mesozoic marine reptiles have been estimated in the past by using a mathematical model of energetic equilibrium during steady swimming. This method suffered from a significant tendency to overestimate speeds of extant cetaceans for no clear reason, which raised questions about the validity of the approach itself. The present study identifies the factors that caused this shortcoming and proposes corrections and some additional modifications. These include the use of more accurate body shape models, updated metabolic rate models, and optimal rather than critical swimming speeds. The amended method successfully approximates published optimal speeds of several extant marine vertebrates, including cetaceans, showing that the basic framework of the energetic approach is valid. With this confirmation, the method was applied to Mesozoic marine reptiles, by assuming three different metabolic rate categories known in extant swimming vertebrates (i.e., average ectothermic, raised ectothermic, and marine endothermic levels). The results support previous inferences about the relative cruising capabilities of Mesozoic marine reptiles (i.e., ichthyosaurs > plesiosaurs > mosasaurs).Stenopterygius, a thunniform ichthyosaur, was probably capable of cruising at a speed at least comparable to those reported for some extant thunniform teleosts with similar diets (~1 m/second).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".