Bibliographic record
Abstract
TORONTO, CANADA—Obtaining human oocytes for embryonic stem (ES) cell experiments raises tricky ethical issues. Researchers want to be sure the donation is voluntary and that women are well-informed of the risks—two areas in which now-discredited stem cell researcher Woo Suk Hwang was faulted. At a recent meeting here, Ann Kiessling, director of the Bedford Stem Cell Research Foundation in Somerville, Massachusetts, described her group's successful efforts to recruit donors. Despite a rigorous screening process that eliminated more than 9 of 10 potential donors, the team had no shortage of oocytes. “We ran out of funds before we ran out of donors,” she says. Nevertheless, her experience suggests that collecting hundreds of oocytes ethically and safely will be expensive and slow.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.298 | 0.125 |
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".