Bibliographic record
Abstract
When British Prime Minister Tony Blair journeyed to San Francisco last August, he bore a simple promotional message for the Bay Area’s bustling biotech hub: The U.K. was open for embryonic stem cell business and prepared to back up its commitment with research cash. “They are interested in working with us in developing the stem cell industry, and we’re working toward a joint California conference to be held in the U.K. in November,” a Blair spokesperson told the BBC, as the British leader prepared to huddle with executives from Genentech, Gilead Sciences, and Cell Genesys. Blair’s position on the controversial research field couldn’t be more different from that of President Bush, who quickly slapped down a bill over the summer that would have expanded federal support for embryonic stem cell work with his first and, thus far, only veto. Extinguishing life in pursuit of science, he counseled, would cross a “moral boundary” that he intends to keep closed. Here in the United States, though, embryonic stem cell research has been showing signs of crossing a host of boundaries, both within the country and around the globe. That point came across clearly when Menlo Park Calif.-based Geron announced in August that it would team up with scientists at the University of Edinburgh in Scotland for its own work in the field. “In addition to the scientific talent and the general receptivity in the U.K. for (human embryonic stem cell) technology, there is funding support,” said David Greenwood, Geron’s chief financial officer. In Singapore, where government support for the field runs strong, the new upstart ES Cell International announced that it was ready to start selling embryonic stem cell lines appropriate for research work. And other countries, including Canada, Israel, and China, have made it clear that they, too, will encourage research into new therapies using embryonic stem cells.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.016 |
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".