The medium-term changes in performance and physiological and anthropometric characteristics following prolonged low-intensity ski trekking in Antarctica: a case study
Bibliographic record
Abstract
This study investigated the performance, physiological, and anthropometric changes in a 28-year-old female following an 1130 km Antarctic ski expedition. Data showed that the participant performed low-intensity ski trekking (LIST, ∼62% maximal heart rate) ∼9 h·day −1 for 46 days of the total of 49 expedition days. Estimations indicated that the mean energy intake during the trek (∼15 MJ·day −1 /∼3589 kcal·day −1 ) was ∼75% greater than that prior to the event (∼8.6 MJ·day −1 /∼2048 kcal·day −1 ), and dietary energy distribution of carbohydrates, fats, and proteins changed from a ∼53/30/17 ratio pre-expedition to a ∼43/48/9 ratio during it. Body mass decreased by 11%, while lean mass was maintained. Time to exhaustion during a short (∼5 min) and long (>40 min) incremental ski trek simulation on a treadmill increased by 29% and 3.6%, respectively. Absolute V̇O 2max (L·min −1 ) was unchanged, but relative V̇O 2max (mL·kg −1 ·min −1 ) increased by 17%. Blood glucose concentration decreased by 8.5%, while lipid levels increased by 11%–35%. Creatine kinase, aspartate aminotransferase, and alanine aminotransferase decreased by 21%, 18%, and 13%, respectively. Thyroid stimulating hormone (TSH) increased by 59%. In conclusion, medium-term prolonged LIST can lead to weight-induced endurance performance enhancement and may improve muscle cell resiliency. A low protein intake during such an event does not appear to affect lean mass negatively, despite a substantial weight loss. Reduced carbohydrate consumption and increased fat intake in relative (%) terms during a medium-duration ski trek can decrease blood glucose concentration and raise lipid levels, respectively. TSH may remain elevated several days after such an expedition, reflecting raised metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".