Relationships Between Blood Lactate and Measures of Anaerobic Capacity in Elite Women's Ice Hockey
Bibliographic record
Abstract
Successful performance in ice hockey relies heavily on anaerobic power, measures of which include blood lactate, sprint performance, acceleration, and vertical jump. No published studies have investigated the relationships between blood lactate and measures of anaerobic power in female elite ice hockey players. PURPOSE: To determine relationships between blood lactate levels, taken at the end of an on-ice Modified Repeat Sprint Skate Test (MRSS), and measures of anaerobic power in elite female ice hockey players. METHODS: Subjects were 80 University of Alberta women players (mean=21.1 yrs, sd=2.6 yrs) measured during pre-season from 1999-2007. Subjects completed preseason tests of off-ice fitness: vertical jump (VJ), 40-yd dash (DASH) and on-ice fitness: speed test (SPEED), 6.10 m acceleration test (ACCEL), Cornering S-Turn agility test (AGIL), and MRSS. Bivariate Pearson correlations were run (SPSS Mac Version 10.0), with a significance level set a priori at p<0.05. RESULTS: No significant relationships were found between post-MRSS blood lactate and any anaerobic capacity measures (r=−0.058 to+0.059, P > 0.05). However, VJ was related to speed and acceleration (r=−.428,−.248, p < .01), DASH was related to SPEED, ACCEL, and AGIL (r= .239 to .332, P p < .05), and on-ice tests were significantly correlated with each other (r = .247 to .866, p<.01). CONCLUSIONS: Blood lactate taken at the end of on-ice MRSS test is not related to on-and off-ice measures of anaerobic power in elite female hockey players; however, better performance on off-and on-ice tests of anaerobic power are significantly related to each other, reflecting common denominators of speed, lower body power, and agility. Perhaps blood lactate is a better index of adaptation to endurance training than a measure of anaerobic power.
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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 | 0.002 |
| 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".