DIFFERENTIATION OF SKIN DERIVED STEM CELLS INTO BLADDER SMOOTH MUSCLE CELLS
Bibliographic record
Abstract
You have accessJournal of Urology1 Apr 2009DIFFERENTIATION OF SKIN DERIVED STEM CELLS INTO BLADDER SMOOTH MUSCLE CELLS Cornelia Toelg, Jeff Biernaskie, Lijun Chi, Karen J Aitken, Alya Ahsan, Norm Rosenblum, Freda Miller, and Darius J Bagli Cornelia ToelgCornelia Toelg More articles by this author , Jeff BiernaskieJeff Biernaskie More articles by this author , Lijun ChiLijun Chi More articles by this author , Karen J AitkenKaren J Aitken More articles by this author , Alya AhsanAlya Ahsan More articles by this author , Norm RosenblumNorm Rosenblum More articles by this author , Freda MillerFreda Miller More articles by this author , and Darius J BagliDarius J Bagli More articles by this author View All Author Informationhttps://doi.org/10.1016/S0022-5347(09)60125-7AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "DIFFERENTIATION OF SKIN DERIVED STEM CELLS INTO BLADDER SMOOTH MUSCLE CELLS." The Journal of Urology, 181(4S), p. 40 © 2009 by American Urological AssociationFiguresReferencesRelatedDetails Volume 181 Issue 4S April 2009 Page: 40 Advertisement Copyright & Permissions© 2009 by American Urological AssociationMetrics Author Information Cornelia Toelg More articles by this author Jeff Biernaskie More articles by this author Lijun Chi More articles by this author Karen J Aitken More articles by this author Alya Ahsan More articles by this author Norm Rosenblum More articles by this author Freda Miller More articles by this author Darius J Bagli More articles by this author Expand All Advertisement PDF downloadLoading ...
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".