Bibliographic record
Abstract
A cancer researcher at Queen's University has given the school a huge boost. Dr. Elizabeth Eisenhauer, director of the Investigational New Drug Program at the Queen's-based National Cancer Institute of Canada (NCIC) Clinical Trials Group, is donating $2.5 million for a new research chair and a further $1.3 million toward a new Cancer Research Institute on the Queen's campus. The Edith Eisenhauer Chair in Clinical Cancer Research is named after Eisenhauer's mother, who died of breast cancer in 1970. “My mother showed me the importance of contributing and making a difference,” says her daughter. The holder of the new chair will also serve as director of the NCIC Clinical Trials Group, while the new institute will house under one roof the 3 NCIC units: the Clinical Trials Group, the Queen's Cancer Research Laboratories and the Radiation Oncology Research Unit. Together, these 3 groups receive more than $15 million a year in research funding. “We'd been talking for some time about coming together,” says Eisenhauer. “This will give us some desperately needed new space. My donation is an opportunity to increase the scope of cancer research and recognize the unique mix of expertise that we have at Queen's.” Queen's does not discuss the source of gifts beyond identifying the donor, but Eisenhauer's donation is widely acknowledged to be a dividend from the university's protection of the intellectual property rights of its researchers.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.516 | 0.287 |
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".