Explaining Performance on Military Tasks in the Canadian Armed Forces: The Importance of Morphological and Physical Fitness Characteristics
Bibliographic record
Abstract
Several occupations apply physical fitness tests to assess occupational physical performance to confirm that their employees meet minimum physical employment standards. Knowledge about factors affecting performance on these physical fitness tests could provide valuable information concerning mode of training. The main purpose of this study was to determine which morphological and/or physiological characteristics could explain overall performance outcome on six complex military tasks used to measure Canadian Armed Forces (CAF) members' occupational fitness. Measures of morphology (height, weight, and body composition) and physical fitness (grip strength, shuttle run time, and plank time etc.), together with performance on six common military tasks were recorded from female (n = 127) and male (n = 294) CAF members. Results showed large differences in both morphology and physical fitness between top and bottom performers in both the male and female group. Despite large differences in morphology, multiple linear regression analyses showed that measures of upper body strength and aerobic capacity could explain a large part of the performance variability in both the male and female group. This study showed that total performance on the CAF military physical fitness test is dependent on physical fitness rather than morphology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".