The effects of a 10-day altitude training camp at 1828 meters on varsity cross-country runners
Bibliographic record
Abstract
International Journal of Exercise Science 10(1): 97-107, 2017. Altitude training has been shown to alter blood lactate (BL) levels due to alterations resulting from acclimatization. This study aims to estimate the impact of altitude training on BL changes immediately following an incremental treadmill test and during recovery before and after 10-day altitude training at approximately 1828 meters. Eight varsity cross-country runners performed an incremental treadmill test (ITT), pre and post-altitude training. Resting and post-warm-up BL values were recorded. During ITT, heart rate (HR), oxygen saturation (SpO2), and time to exhaustion were monitored. BL was also measured post-ITT at 0, 2, 4, 6, and 8 minutes. The average of all BL values was higher following altitude intervention (8.8 ± 4.6 mmol/L) compared to pre-intervention (7.4 ± 3.3 mmol/L). These differences were statistically significant (t(6) = -2.40, p = .026). BL immediately (0 minutes) after the ITT was higher following the altitude intervention (13.6 ± 3.6 mmol/L) compared to pre-intervention (9.7 ± 3.8 mmol/L) and was statistically significant (t(7) = -3.30, p = .006). Average HR during the ITT was lower following the altitude intervention (176.9 ± 11.1 bpm) compared to pre (187 ± 9.5 bpm), these differences were statistically significant (t(28)= 18.07, p=
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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".