In <i>the</i> NEWS: A ROUNDUP OF NEWS AND INFORMATION FROM OUR COMMUNITY
Bibliographic record
Abstract
C ancer research was the big winner of Ohio's Pelotonia bicycle race last year, which generated more than $19 million to further studies into development of a cutting-edge leukemia drug, among other therapeutic initiatives.In all, nine research projects received funding-dubbed idea grants-from proceeds of the cycling event, a grassroots effort launched 5 years ago in Columbus, Ohio to raise money for cancer studies based at The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (OSUCCC-James).The funding is generated by riderraised contributions and corporate sponsors.Overall, The Pelotonia race has raised $61 million for cancer research at the university since it was established 5 years ago."We get more and more riders every year, and the money goes to recruitment, equipment, and research," says Peter Shields, MD, deputy director of OSUCCC-James."We're lucky, we're a small enough city that we don't have as much competition from other cancer and medical centers as bigger cities do and people know each other and want to support 'The James.'"
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.098 | 0.080 |
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".