Bibliographic record
Abstract
Tissue engineering and cellular therapies, either on their own or in combination with therapeutic gene delivery, have the potential to significantly impact medicine. Implementation of technologies based on these approaches requires a readily available source of cells for the generation of cells and tissues outside a living body. Because of their unique capacity to regenerate functional tissue for the lifetime of an organism, stem cells are an attractive "raw material" for multiple biotechnological applications. By definition they are self-renewing because on cell division they can generate daughter stem cells. They are also multipotent because they can differentiate into numerous specialized, functional cells. Recent findings have shown that stem cells exist in most, if not all, tissues, and that stem cell tissue specificity may be more flexible than originally thought. Although the potential for producing novel cell-based products from stem cells is large, currently there are no effective technologically relevant methodologies for culturing stem cells outside the body, or for reproducibly stimulating them to differentiate into functional cells. A mechanistic understanding of the parameters important in the control of stem cell self-renewal and lineage commitment is thus necessary to guide the development of bioprocesses for the ex vivo culture of stem cells and their derivates.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".