Locomotor behavior and hearing sensitivity in an early lagomorph reconstructed from the bony labyrinth
Bibliographic record
Abstract
Abstract The structure of the bony labyrinth is highly informative with respect to locomotor agility (semicircular canals [SCC]) and hearing sensitivity (cochlear and oval windows). Here, we reconstructed the agility and hearing sensitivity of the stem lagomorph Megalagus turgidus from the early Oligocene of the Brule Formation of Nebraska (USA). Megalagus has proportionally smaller SCCs with respect to its body mass compared with most extant leporids but within the modern range of variability, suggesting that it was less agile than most of its modern relatives. A level of agility for Megalagus within the range of modern rabbits is consistent with the evidence from postcranial elements. The hearing sensitivity for Megalagus is in the range of extant lagomorphs for both low‐ and high‐frequency sounds. Our data show that by the early Oligocene stem lagomorphs had already attained fundamentally rabbit‐like hearing sensitivity and locomotor behavior, even though Megalagus was not a particularly agile lagomorph. This is likely because Megalagus was more of a woodland dweller than an open‐habitat runner. The study of sensory evolution in Lagomorpha is practically unknown, and these results provide first advances in understanding the primitive stages for the order and how the earliest members of this clade perceived their environment.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".