High incidence of Hepatitis C virus infection observed in the PROUD study of HIV pre‐exposure prophylaxis
Bibliographic record
Abstract
Evidence suggests that new vocabulary undergoes a period of strengthening and integration offline, particularly during sleep.Practical questions remain, however, including whether learning closer to bedtime can optimize consolidation, and whether such an effect varies with vocabulary ability.To examine this, children aged 8-12-years-old (n 59) were trained on written novel forms (e.g.BANARA) in either the morning (long delay) or the evening (short delay).Immediately after training and the next day, lexical competition (a marker of integration) was assessed via speeded semantic decisions to neighbouring existing words (e.g.BANANA); explicit memory was measured via recognition and recall tasks.There were no main effects indicating performance changes across sleep for any task, counter to studies of spoken word learning.However, a significant interaction was found, such that children with poorer vocabulary showed stronger lexical competition on the day after learning if there was a short delay between learning and sleep.Furthermore, while poorer vocabulary was associated with slower novel word recognition speed before and after sleep for the long delay group, this association was only present before sleep for the short delay group.Thus, weak vocabulary knowledge compromises novel word acquisition, and when there is a longer period of post-learning wake, this disadvantage remains after a consolidation opportunity.However, when sleep occurs soon after learning, consolidation processes can compensate for
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".