Keep calm and hang on: EMG activation in the forelimb musculature of three-toed sloths (<i>Bradypus variegatus</i>)
Bibliographic record
Abstract
Sloths exhibit below branch locomotion whereby their limbs are loaded in tension to support the body weight. Suspensory behaviors require both strength and fatigue resistance from the limb flexors; however, skeletal muscle mass of sloths is reduced compared to other arboreal mammals. Although suspensory locomotion demands that muscles are active to counteract the pull of gravity, it is possible that sloths minimize muscle activation and/or selectively recruit slow motor units to maintain support, thus indicating neuromuscular specializations to conserve energy. Electromyography (EMG) was evaluated in a sample of three-toed sloths (B. variegatus: N=6) to test this hypothesis. EMG was recorded at 2000 Hz via fine-wire electrodes implanted into two suites of four muscles in the left forelimb while sloths performed suspensory hanging (SH), suspensory walking (SW), and vertical climbing (VC). All muscles were minimally active for SH. During SW and VC, sloths moved slowly (Duty Factor: 0.83) and activation patterns were consistent between behaviors; the flexors were activated early and for a large percentage of limb contact, whereas the extensors were activated for shorter burst durations on average and showed biphasic (contact and swing) activity. Muscle activities were maximal for the elbow flexors and lowest for the carpal/digital flexors, and overall activity was significantly greater for SW and VC compared with SH. Wavelet analysis indicated high mean EMG frequencies from the myoelectric intensity spectra coupled with low burst intensities for SH, although the opposite pattern occurred for SW and VC, with the shoulder flexors and elbow flexor, m. brachioradialis, having extremely low mean EMG frequencies that are consistent with recruitment of slow fibers. Collectively, these findings support the hypothesis and suggest that sloths may selectively recruit smaller, fast motor units for suspensory postures but have the ability to offset the cost of force production by recruitment of large, slow motor units during locomotion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".