The Effect Of Aerobic Capacity On Lactate Production And Elimination In Ice Hockey Players
Bibliographic record
Abstract
PURPOSE: To measure the effect of VO2max on lactate production and elimination following a maximal 12 time repeat 20 meter stop and go shuttle skate. METHODS: Maximal aerobic capacity was measured on-ice as described by Leone et al and adapted by Whittom et al for direct measurements using a portable metabolic cart (K4B2, Cosmed, It). Briefly, players skated over 45 meters at a cadence given by an auditory cue at the beginning (goal line), midway (red line) and the end (opposite goal line) of the ice sheet for one minute (stop and go at the goal lines). At the end of one minute a 30 second rest period was taken and the players would then resume skating at quicker pace (set by the auditory cue) following the same pattern as described above. The player would continue this procedure until reaching maximal skating speed. The test would stop when the player was no longer able to keep pace with the auditory cue. This stopping point was defined as VO2max. After two days of rest the players were instructed to perform an all out on-ice wingate, which was defined as skating back and forth (stop and go) as quickly as possible over 20 meters 12 times. Immediately following the on-ice wingate the players were instructed to return to the bench for a 45′ passive recovery. Lactate measurements were taken (Lactate Pro) at 1′, 3′, 10′, 15′ and 45′ following the on-ice wingate. RESULTS:Table: Lactic acid concentration (mmol/L) following the on-ice wingateCONCLUSION: Players with higher VO2max values (>53 ml/kg/min) had a larger lactic acid production combined with a quicker rate of elimination. This is an important observation that indicates the possibility for a quicker recovery between shifts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".