Bibliographic record
Abstract
3. This congress was multidisciplinary with key notes and parallel sections on both life sciences and social sciences. We received communications about physical preparation, physiology, biomechanics, sport medicine, rehabilitation as well as history, sociology, management, psychology, teaching.The following world-class experts gave six key notes presentations:• Milena Parent (Canada) -Governance and legacy of the YOG.• Hans-Christer Holmberg (Sweden) -Biomechanics and physiology in nordic skiing.• Andrew Denning (USA) -History of the human-environment relationships in the Alps through the sport of skiing.• Erich E. Muller (Austria) -Biomechanics and prevention of injuries in alpine Skiing.• Oyvind Sandbakk (Norway) -Norwegian success in winter sports. Despite that the research topic was multidisciplinary in line with the content of the congress, only articles in exercise physiology, biomechanics and nutrition are published. years old -physiological characteristic (i.e., high total haemoglobin mass expressed in g/kg) is a relevant predictor of success at senior level in endurance sports as cross-country ski or triathlon.Finally, this research topic includes three reviews (compression garments, injuries and nutritional considerations) relevant to all winter sports:
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.036 | 0.030 |
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".