Body Mass Index, Physical Activity, and Smoking in Relation to Military Readiness
Bibliographic record
Abstract
The objective of the study was to analyze the influence of excess weight, regular physical activity, and smoking on the military readiness of the Belgian Armed Forces in a cross-sectional online survey. A multinomial logistic regression was used to study the influence of modifiable risk factors on participation in the physical fitness test. In our study population (n = 4,959), subjects with a body mass index higher than 25 kg/m(2), smokers, and subjects with a lower level of vigorous physical activity were significantly more likely to have failed the physical fitness test. In the Belgian Armed Forces, serious efforts should be made to encourage vigorous physical activity, smoking cessation, and weight loss to preserve our military readiness. Instead of relying on civilian public health interventions, Belgian Defense should develop its own specific approaches to prevent weight gain, improve physical fitness, and influence smoking attitude.
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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 | 0.001 | 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 | 0.001 |
| 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".