Different neurotransmitters are included the exercise fatigue in different humidity and heat environments
Bibliographic record
Abstract
This study examined whether different neurotransmitters are presented at exercise fatigue in different temperature and relative humidity (RH) environments in athletes. Eight trained male athletes performed exercise in five different environmental conditions: 21°C/20% RH (Normal); 33°C/20 % RH (Hot 20%), 33°C/40% RH (Hot 40%), 33°C/60% RH (Hot 60%), and 33°C/80% RH (Hot 80%). Exercise group performed VO 2 max test in five conditions. Blood samples were taken pre‐ and post‐exercise and analyzed for noradrenaline (NA), adrenaline (Ad), dopamine (DA), serontonin (5‐HT), 5‐hydroxyindoleacetic acid (5‐HIAA), prolactin (PRL). Compare to Normal condition, Hot 20%, Hot 40% and Hot 80% have lower VO 2 max ( P < 0.05). A comparison between the means indicated that in Hot 20%, Hot 40%, Hot 60% and Hot 80% conditions the RPEmax was higher than the Normal condition ( P < 0.01 or P < 0.05). There was a significant effect for time in NE ( P < 0.0001), PRL ( P < 0.0001), 5‐HT ( P = 0.002), 5‐HIAA ( P = 0.029), DA ( P = 0.016) during exercise in different conditions. However, Ad did not show any significant effect between pre and post exercise ( P >; 0.05). In different humidity and heat environments, exercise fatigue is determined by the collaboration of the different neurotransmitter systems, with the most important role possibly being for the NE, 5‐HT and DA. This work was supported by Grants from Ministry of Science and Technology of the People's Republic of China (2010–05).
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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) | 0.001 | 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".