Pre-season Improvements In Economy, Cardiorespiratory Function And Strength Exhibited In An Elite Multi-sport Endurance Athlete
Bibliographic record
Abstract
Many physiological measures can be used to monitor training of endurance athletes. These measures range from maximal oxygen uptake (VO2max) to economy of movement. We used a variety markers to monitor changes in physiological function in an elite (e.g., World University Games) college female athlete competing since 2007 in cross-country (xc-) running, xc-skiing and biathlon. We monitored a variety of physiological variables in a female endurance athlete from the start of off-season (OS) to the conclusion of pre-Season (PS). PURPOSE: To measure changes in running economy, cardiorespiratory function and strength through 5 months of training. METHODS: Participant, one female college multi-sport endurance athlete. Training took place from OS through PS 5-7 days/week with a regimen of running, roller-skiing, cycling, weight lifting, and plyometrics. Testing began at the start of OS (May 10) concluding at the end of PS (Sept. 10). Tests included: maximal exercise variables (e.g., VO2max); morning, submaximal, and maximum HRs; running economy; and upper and lower body strength. RESULTS: The following changes from OS to the end of PS were noted: body weight (-0.18%), morning HR (0.0), VO2max absolute (+3.4%) and relative (+3.5%), VE (+4.2%), HRmax (-1.1), leg press (+11.1), and bench press (+23.8%). HRs (±beats/min) were reduced during and following (Rec) a 1-mile run test at 7.5 miles/hr (0.5 mile=-7, 1 mile=-10, 1 min Rec=-18, 3 min Rec=-8, 5 min Rec=-6). See Table 1 - running economy. CONCLUSION: Large improvements occurred from OS to the end of PS in this elite multi-sport endurance athlete, with the most important changes being running economy and strength.Table 1: Oxygen consumption (VO2 mL/kg/min) at submaximal running speeds.
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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 | 0.000 |
| 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) | 0.002 | 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".