Aerobic power and anaerobic threshold of male rowers / by Argyrios V. Fotis. --
Bibliographic record
Abstract
The purpose of this study was to investigate the endurance component \nof off-season rowing performance in male rowers before and after an \n8-week training period. Specific attention was given to laboratory \nassessment of aerobic power (AP) and anaerobic threshold (AT) as they \nwere affected by the above training period. \nA single subject case study research design was employed, which \ninvolved pre- and post-training tests, of seven male rowers of national \nand provincial calibre. Following the pre-training test, individualized \ntraining programs were designed involving continuous and interval \nendurance training. The training intensity for continuous training (CT) and \ninterval training (IT) was based on a percentage below (AT -10% VO[subscript]2 max) and above (AT +10 to 25% VO[subscript]2 max) the subject's AT. The training intensity was monitored through heart rate count. The training was carried out on rowing ergometers (Concept II), 3 times per week, each session lasting 50 to 60 minutes. After the training period, VO2 max L min[superscript]1 and VO[subscript]2 M1kg[superscript]1 min[superscript]1 at AT increased in all subjects between 1.8% and 22.9% and -4.2% to 16.3%, respectively. As well, the maximum \nrowing ergometer performance, power output, maximum workload, and \ntime before reaching AT increased. \nHigh levels of AT among oarsmen are attributed to the specific nature \nof training regimens which may have increased the oxidative capacity of \nmuscle fibers and the cardiorespiratory transport system. Measurement of \nHR at AT could provide the coach and the oarsman with an objective \nmethod of monitoring the intensity of training. These results demonstrate \nthat the AT in rowers is profoundly influenced by endurance training.
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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) | 0.001 | 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) | 0.009 | 0.003 |
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".