Expand and Regularize Federal Funding for Human Pluripotent Stem Cell Research
Bibliographic record
Abstract
Potential therapies associated with stem cell research have captured the imagination of the public.The idea that some types of serious health problems could be corrected by growing our own cells anew is compelling.Nonetheless, the seminal research in this area relied upon extraction of cells from human embryos.The source of these pluripotent cells, in itself, raised ethical objections tied to the sanctity of life that have impacted government regulation and funding of research in this area.Further, the advent of human embryonic stem cell research at times presented scientists with uncomfortable ethical choices in the pursuit of often very fundamental scientific research.Scientists have since developed methods of reprogramming cells from adults into induced pluripotent stem cells so that they can also be differentiated for alternative purposes in the body, but questions remain about permissible sources and uses of these cells.Of course, all of this research comes at a considerable cost that should be weighed against both current and realistic future advances of the technology.Thus, stem cell science lies at the intersection of the advancement of technology, societal concepts of ethical behavior, and the role of government.In this Point/Counterpoint, I have invited two leading groups of authors to discuss the complex issues related to stem cell research, as well as what might generally be learned from them by addressing the following questions:
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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 | 0.071 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".