Reply to Discussion of “Effect of plasma donation and blood donation on aerobic and anaerobic responses in exhaustive, severe-intensity exercise”
Bibliographic record
Abstract
We appreciate the comments from Dr. Mora-Rodriguez (Mora-Rodriguez 2014). He succinctly summarized the similarities between our study (Hill et al. 2013) and his (Mora-Rodriguez et al. 2012). He identified some apparent discrepancies in our findings and his, and he noted that there were important differences between our methods and his. We used an exhaustive, severe-intensity task, which engendered a maximal aerobic response (VO2max) that was associated, presumably, with maximal cardiac output and maximal oxygen extraction. After blood donation, neither cardiac output nor oxygen extraction could increase further to offset reductions in oxygen carrying capacity or cardiac output consequent to the loss of hemoglobin and blood volume. Thus, with severe-intensity exercise, the oxygen uptake (the VO2max) decreased after blood donation, meaning that the rate of aerobic energy provision was reduced, meaning that the rate of anaerobic energy provision must have been greater. The anaerobic capacity (the amount of energy provided anaerobically), which was quantified by the oxygen deficit and also by the peak blood lactate concentration, was not affected by blood donation (anaerobic capacity was affected by plasma donation). So, time to exhaustion was reduced after blood donation. Dr. Mora-Rodriguez argues that the “anaerobic energy contribution to exercise is increased after blood donation based on [his] submaximal exercise results”. We argue that the rate of anaerobic energy contribution to severe-intensity exercise is increased after blood donation, but the amount is unaffected. We have every confidence that his findings regarding the effects of blood donation on responses to heavy exercise and our findings regarding the effects of blood donation on responses to severe-intensity exercise can co-exist. They are complementary.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.042 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".