Heart Responses In Elite Women Basketball Referees During The 2013 Eurobasket Championship
Bibliographic record
Abstract
PURPOSE: This investigation analyzed the cardiovascular characteristics in a group of experienced elite women basketball referee during Eurobasket 2013. Leitch et al. (2004, 2008) have published various papers regarding heart rate (HR) responses among referees at various levels of play. However, to author’s knowledge, no studies have investigated data among women referees in elite competition. METHODS: Nine elite female basketball referees (38 ± 3 years) were analyzed with a random sample of 11 matches during the 2013 women´s Eurobasket Championship. The height, body mass and ∑ 6 skinfolds were measured. Body Fat was calculated by Carter equation. Cardiovascular response was determined by HR recordings from immediately prior to the start of each Quarter, during the match and until immediately following the Quarter, including all rest breaks. RESULTS: In our study, the average HR was 156.8±9.3 p/min, 86.2±5.0% of maximum HR, and the majority of the match time was spent at a more strenuous exercise intensity (70-89% HRmax) greater than the 70% HRmax previously reported. In addition, significant decreases in HR response were seen when comparing the fourth quarter of the match to the second quarter and to the first quarter (P<0.05) as well as when comparing the third quarter to the second quarter (p<0.05). CONCLUSIONS: Similar relative exercise intensity was demonstrated regardless of player gender or officiating scheme (2-referee or 3-referee plans. Further study is needed to document the the physiological characteristics of elite female basketball referees over an entire season.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".