Scaling of sensorimotor delays in terrestrial mammals
Bibliographic record
Abstract
Whether an animal is trying to escape from a predator, avoid a fall or perform a more mundane task, the effectiveness of its sensory feedback is constrained by sensorimotor delays. Here, we combine electrophysiological experiments, systematic reviews of the literature and biophysical models to determine how delays associated with the fastest locomotor reflex scale with size in terrestrial mammals. Nerve conduction delay is one contributor, and increases strongly with animal size. Sensing, synaptic and neuromuscular junction delays also contribute, and we approximate each as a constant value independent of animal size. Muscle's electromechanical and force generation delays increase more moderately with animal size than nerve conduction delay, but their total contribution exceeds that of the four neural delays. The sum of these six component delays, termed total delay, increases with animal size in proportion toM0.21—large mammals experience total delays 17 times longer than small mammals. The slower movement times of large animals mostly offset their long delays resulting in a more modest, but perhaps still significant, doubling of their total delay relative to movement duration when compared with their smaller counterparts. Irrespective of size, sensorimotor delay is likely a challenge for all mammals, particularly during fast running.
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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 | 0.005 |
| 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 | 0.000 | 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".