Young Researcher Award Abstracts
Bibliographic record
Abstract
Background: Previous research investigating the effects of exercise-related stress on sleep has found that timing of exercise, exercise intensity and periodization of training adversely affects sleep quality.Anecdotally, it has been reported that dehydration may influence sleep [1][2][3][4][5].Little is known as to whether exercise-induced dehydration or method of rehydration has any effect on quality of sleep following prolonged exercise in hot conditions.Objective: The purpose of this study was to compare ad libitum versus prescribed (150% of sweat losses) fluid replacement on subjective quality and objective measures of sleep following exerciseinduced dehydration.Methods: Eleven healthy, recreationally active males (mean±SD; age, 22 ± 3 y; height, 178 ± 6 cm; VO 2max , 54.3 ± 5.4 ml•kg -1 •min -1 ; body fat, 11.6 ± 3.9%) completed three randomized exercise sessions: euhydrated arrival + fluid replacement (EUR), euhydrated arrival + no fluid (EUD) and hypohydrated arrival + no fluid (HYD) in hot conditions (ambient temperature, 35.3 ± 0.6°C and relative humidity, 31.3 ± 2.0%).Exercise sessions consisted of six 30-min cycles of treadmill exercise (8 min at 40% VO 2max , 8 min at 60% VO 2max , 8 min at 40% VO 2max and 6 min of passive rest each) followed by 60-min of passive rest.Following exercise, participants were randomly assigned to either a prescribed or ad libitum rehydration group and returned to the laboratory 24-30 h following each exercise session.Participants donned a wrist-worn activity tracker the night prior (PRE) and following (POST) each session to measure sleep efficiency; total time spent sleeping; and time spent in deep, light and rapid eye movement (REM) sleep.Subjects also subjectively assessed their sleep quality using the Karolinska Sleep Diary (KSD).The individual components of the KSD were summed to tabulate an overall subjective sleep quality measure (KSD TOTAL ).A mixed design (condition x trial x time) repeated measures ANOVA with Tukey
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.630 | 0.480 |
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".