0083 The Multiplicity of Dreams Reported from Nightly Awakenings
Bibliographic record
Abstract
Abstract Introduction Dream recall has high individual variability affected by a wide spectrum of methodological variables. The possibility that multiple dreams are typically experienced each night has received minimal study. Methods 51 subjects were recruited from college psychology classes (35 F/16 M, mean age 22.2). The intake questionnaire queried subject dream recall varying from no dream recall=1, to multiple dreams from each night of sleep=7. A smart phone app with a sixty-minute delay was then used at home to induce hourly awakenings utilizing a bed side check list to notate: no dreaming or defined forms of dreaming including dreams with content, white dreams (awareness of dreaming without content), and nightmares. 44 subjects (86%) competed the full protocol of 8 dream checkoff reports. Reasons for discontinuation: concern with sleep loss (1), technical issues with alarm (2), early rising for work (1), spousal complaint (1). Results The mean number of dreams reported on intake using Likert scale was 4.647 (1-3/week). Checkoff dream recall reports were obtained from 395 total awakenings: no dreaming (#150 - 38%); content dreams #98 (25%); white dreams #74 (19%); and nightmares # 14 (3.5%). Some form of dreaming was reported on 186 (47%) of awakenings. Mean number of dreams reported was 4.0/individual during the night of study. One individual reported no dream recall on any awakening and 2/51 reported dreams with content on every awakening. 6/51 (12%) reported some form of dreaming at every awakening. For individuals completing the full protocol, non-dreaming was reported significantly more often on the first two hourly awakenings compared to the last 2 awakenings (X2=24.7, p< 0.001). There was a statistically significant association between reported intake questionnaire dream recall and the number of dreams reported on checklist during the night of study (Pearson r = 0.571, p < 0.001). Conclusion This small pilot study utilizes a novel, low-tec, safe, and easily expandable protocol to study changes dream recall across the night. Individuals reporting a higher frequency of dream recall on intake had a significantly higher level of dreaming reported from serial awakenings. 12 % of subjects reported some form of dreaming on every awakening. Support (if any) none
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".