Physiological responses and energy expenditure during competitive fencing
Bibliographic record
Abstract
Fencing is an Olympic sport in which athletes fight one against one using bladed weapons. Contests consist of three 3-min bouts, with rest intervals of 1 min between them. No studies investigating oxygen uptake and energetic demand during fencing competitions exist, thus energetic expenditure and demand in this sport remain speculative. The aim of this study was to understand the physiological capacities underlying fencing performance. Aerobic energy expenditure and the recruitment of lactic anaerobic metabolism were determined in 15 athletes (2 females and 13 males) during a simulation of fencing by using a portable gas analyzer (MedGraphics VO2000), which was able to provide data on oxygen uptake, carbon dioxide production and heart rate. Blood lactate was assessed by means of a portable lactate analyzer. Average group energetic expenditure during the simulation was (mean ± SD) 10.24 ± 0.65 kcal·min(-1), corresponding to 8.6 ± 0.54 METs. Oxygen uptakeand heart rate were always below the level of anaerobic threshold previously assessed during the preliminary incremental test, while blood lactate reached its maximum value of 6.9 ± 2.1 mmol·L(-1) during the final recovery minute between rounds. Present data suggest that physical demand in fencing is moderate for skilled fencers and that both aerobic energy metabolism and anaerobic lactic energy sources are moderately recruited. This should be considered by coaches when preparing training programs for athletes.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".