Une avancée technique pour comprendre la dynamique de l’horloge de segmentation
Bibliographic record
Abstract
355 10. Winner B, Melrose HL, Zhao C, et al. Adult neurogenesis and neurite outgrowth are impaired in LRRK2 G2019S mice. Neurobiol Dis 2011 ; 41 : 706-16. 11. Coulombel L. Reprogrammation nucleaire d’une cellule differenciee : on efface tout et on recommence. Med Sci (Paris) 2007 ; 23 : 667–70. 12. Maury Y, Gauthier M, Peschanski M, Martinat C. Human pluripotent stem cells: opening key for pathological modeling. Med Sci (Paris) 2011 ; 27 : 443-6. 13. Laustriat D, Gide J, Hechard C, Peschanski M. Les cellules souches embryonnaires et le pharmacologue. Med Sci (Paris) 2009 ; 25 (suppl 2) : 32-8. 5. Tiscornia G, Vivas EL, Izpisua Belmonte JC. Diseases in a dish: modeling human genetic disorders using induced pluripotent cells. Nat Med 2011 ; 17 : 1570-6. 6. Papp B, Plath K. Reprogramming to pluripotency: stepwise resetting of the epigenetic landscape. Cell Res 2011 ; 21 : 486-501. 7. Liu GH, Barkho BZ, Ruiz S, et al. Recapitulation of premature ageing with iPSCs from Hutchinson-Gilford progeria syndrome. Nature 2011 ; 472 : 221-5. 8. Marchetto MC, Carromeu C, Acab A, et al. A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell 2010 ; 143 : 527-39. 9. Liu GH, Qu J, Suzuki K, et al. Progressive degeneration of human neural stem cells caused by pathogenic LRRK2. Nature 2012 ; 491 : 603-7. REFERENCES
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".