Bibliographic record
Abstract
Twenty-five years ago, most hematologists understood the concept of stem cells through the pioneering work of Till and McCullough, who created assays for the properties of self-renewal and the emergence of cells of different lineage fates from single cells marked by nonlethal cytogenetic changes induced by low-dose irradiation. Clinical use of stem cells—the collection of bone marrow cells including by sheer volume the desired subcomponent of pluripotent stem cells—and the use of this product to reconstitute hematopoiesis in patients undergoing ablative stem cell transplantation were in their infancy. Looking back over all the articles published in Stem Cells during the past 25 years, one can see clearly the major streams of progress. The ever more powerful isolation and enrichment for populations of embryonic or adult stem cells and the extension to cancer stem cells have been critical achievements. A novel method of stem cell enrichment has emerged through the development of zebrafish (Danio rerio) and the nematode (Caenorhabditis elegans) models with their markedly limited total number of cells undergoing self-renewal and cell fate determinations. Perhaps the primary current of change has been the slow, steady identification of the transcriptional factors and the genetic regulatory networks operating in various stem cells and stem cell niches. The molecular bases for concepts such as “plasticity” and “stemness,” if not yet completely understood, are now coming into focus.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".