The Effects of Aromatherapy, Meditation, and Blue Light on Sleep Quality
Bibliographic record
Abstract
Sleep insomnia, which reduces the effectiveness of sleep, is a large problem in America that affects individuals with a range of ages. Nearly 60 million people suffer from insomnia in America every year. My goal was to see if the use of aromatherapy, reduction of blue light, and practice of meditation before bed would increase sleep quality. More specifically, I will be focused on increasing the time spent in deep sleep and reducing the times one wakes up during the night. To test my hypothesis, I used the Fitbit Versa to monitor the sleep patterns of the participants of three people with and then the same three people without the pre-bed routines that may affect their sleep. While the results showed that aromatherapy meditation did improve the sleep of the participants, allowing the participant to sleep without waking up as many times are normal and feel more refreshed upon waking, the reduction of blue light had no significant impacts on the participant’s quality of sleep.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".