Perspectives on resilience for military readiness and preparedness: Report of an international military physiology roundtable
Bibliographic record
Abstract
Modern warfare operations often occur in volatile, uncertain, complex, and ambiguous (VUCA) environments accompanied by physical exertion, cognitive overload, sleep restriction and caloric deprivation. The increasingly fast-paced nature of these operations requires military personnel to demonstrate readiness and resiliency in the face of stressful environments to maintain optimal cognitive and physical performance necessary for success. Resiliency, the capacity to overcome the negative effects of setbacks and associated stress on performance, is a complex process involving not only an individual's physiology and psychology, but the influence of factors such as sex, environment, and training. The purpose of this moderated roundtable was to address five key domains of resiliency in a point/counterpoint format: physiological versus psychological resiliency, sex differences, contributions of aerobic and strength training, thermal tolerance, and the role of nature versus nurture. Each speaker was given three minutes to present and the moderator facilitated questions and discussion following the panel's presentation. The interconnectedness of the five domains highlights the need for an interdisciplinary approach to understand and build resilience to enhance military performance.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".