A Global Assessment of Stem Cell Engineering
Bibliographic record
Abstract
Over the last 2 years a global assessment of stem cell engineering (SCE) was conducted with the sponsorship of the National Science Foundation, the National Cancer Institute at the National Institutes of Health, and the National Institute of Standards and Technology. The purpose was to gather information on the worldwide status and trends in SCE, that is, the involvement of engineers and engineering approaches in the stem cell field, both in basic research and in the translation of research into clinical applications and commercial products. The study was facilitated and managed by the World Technology Evaluation Center. The process involved site visits in both Asia and Europe, and it also included several different workshops. From this assessment, the panel concluded that there needs to be an increased role for engineers and the engineering approach. This will provide a foundation for the generation of new markets and future economic growth. To do this will require an increased investment in engineering, applied research, and commercialization as it relates to stem cell research and technology. It also will require programs that support interdisciplinary teams, new innovative mechanisms for academic-industry partnerships, and unique translational models. In addition, the global community would benefit from forming strategic partnerships between countries that can leverage existing and emerging strengths in different institutions. To implement such partnerships will require multinational grant programs with appropriate review mechanisms.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".