Le sommeil c’est bon pour le cerveau
Bibliographic record
Abstract
Pourquoi le sommeil est-il si important ? Pourquoi avons-nous fréquemment des insomnies ? Le professeur Steven Laureys, neurologue mondialement connu, mène depuis plus de vingt-cinq ans des recherches révolutionnaires sur les états de conscience. Grâce à la neuro-imagerie, il étudie le cerveau pendant le sommeil. Dans ce livre, le docteur Laureys nous donne des clés pour passer de bonnes nuits de sommeil. Il nous explique pourquoi dormir est essentiel pour notre cerveau et notre santé, et que faire en cas de difficultés d’endormissement, de sommeil agité, de fatigue, de ronflements, de somnambulisme, de paralysie du sommeil, de cauchemars, de rêves lucides… Le docteur Laureys nous montre que le sommeil, c’est bon pour le cerveau ! « Ce livre va changer vos nuits pour toujours » Pr Manuel Schabus Directeur du laboratoire du sommeil, Université de Salzbourg « Une nouvelle perspective sur le monde fascinant du sommeil » Pr Charles M. Morin Ancien président de la World Sleep Society, directeur du laboratoire du sommeil, Université Laval (Canada)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.012 |
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".