The physiological consequences of and recovery following the Australian Special Forces Selection Course
Bibliographic record
Abstract
This study aimed to determine the energy requirements, physiological consequences, and recovery rate from the Australian Special Forces Selection Course. Ninety-three male soldiers (mean ± SD, 28.1 ± 3.6 years, 1.81 ± 0.1 m, 85.1 ± 8.1 kg) volunteered for this study. Body composition via dual-energy X-ray absorptiometry, hormones and resting metabolic rate were assessed before, immediately after, and at 1, 3, 5, and 8 weeks post-course. Energy expenditure, assessed via doubly-labelled water during the first 10 days of the course significantly exceeded energy intake (expenditure: 7680 ± 1095 kcal . day –1 , intake: 3859 ± 704 kcal . day – 1 ). Body mass (Δ –6.8 ± 1.9 kg, p < 0.01), fat mass (Δ –4.2 ± 1.0 kg, p < 0.0001) and lean mass (Δ –3.0 ± 1.7 kg, p < 0.0001) were significantly reduced in response to the course and returned to baseline 1–3 weeks post-course. Total testosterone, free testosterone, free triiodothyronine, free thyroxine and insulin like growth factor-1 significantly ( p < 0.001) declined following the course, while cortisol and sex hormone binding globulin increased ( p < 0.001). All hormones, except insulin like growth factor-1, returned to baseline concentrations within 1–3 weeks post-course. Resting metabolic rate decreased ( p < 0.01) in response to the course, and subsequently rebounded above baseline levels at 1 week post-course. The Special Forces Selection Course involved high energy output and a substantial caloric deficit, resulting in body mass loss and significant hormonal disruption that took weeks to recover. These results highlight the energy requirements, physiological consequences, and recovery processes from the Australian Special Forces Selection Course.
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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 | 0.001 |
| 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 | 0.001 |
| 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".