Energy Cost and Mechanical Work of Walking during Load Carriage in Soldiers
Bibliographic record
Abstract
UNLABELLED: In the military context, soldiers carry equipments of total mass often exceeding 30%-40% of their body mass (BM) and complexly distributed around their body (backpack, weapons, electronics, protections, etc.), which represents severe load carrying conditions. PURPOSE: This study aimed to better understand the effects of load carriage on walking energetics and mechanics during military-type walking. METHODS: Ten male infantrymen recently retired from the French Foreign Legion performed 3-min walking trials at a constant speed of 4 km·h(-1) on an instrumented treadmill, during which walking pattern spatiotemporal parameters, energy cost (C(W)), external mechanical work (W(ext)), and the work done by one leg against the other during the double-contact period (W(int,dc)) were specifically assessed. Three conditions were tested: (i) light sportswear (SP, reference condition considered as unloaded), (ii) battle equipment (BT, ∼22 kg, ∼27% of subjects' BM, corresponding to a military intermediate load), and (iii) road march equipment (RM, ∼38 kg, ∼46% of subjects' BM, corresponding to a military high load). RESULTS: Repeated-measures ANOVA showed that military equipment carriage significantly (i) altered the spatiotemporal pattern of walking (all P < 0.01), (ii) increased absolute gross and net CW (P < 0.0001), and (iii) increased both absolute and mass-relative W(ext) (P < 0.01) and W(int,dc) (P < 0.0001) but did not alter the inverted pendulum recovery or locomotor efficiency. CONCLUSIONS: Military equipments carriage induced significant changes in walking mechanics and energetics, but these effects appeared not greater than those reported with loads carried around the waist and close to the center of mass. This result was not expected because the latter has been hypothesized to be the optimal method of load carriage from a metabolic standpoint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".