Bibliographic record
Abstract
The past decade has seen dramatic progress in our understanding of the basic cell and molecular biology of breast cancer. However, the translation of this basic science knowledge and ideas has been impeded by the limited numbers of clinician-scientists with skills to effectively collaborate with basic scientists and to accurately interpret tissue pathology, quality and the cellular composition of heterogeneous tissue samples subjected to molecular study. Beyond this there is the problem of access to appropriate human tissue samples. The Pi's overall goal is the improvement in our current ability to predict individual risk of development of invasive disease and to predict further progression of invasive disease in terms of resistance to therapies. The specific aims of the PI are (1) continue to advance the two general avenues of research that are currently ongoing in the laboratory and which have direct relevance to important clinical problems in the management of early pre-invasive disease and the therapy of later advanced disease, (2) continue to direct the Manitoba Breast Tumor Bank and offer clinical pathology expertise and advice to many investigators who seek access to appropriate tissues to test their ideas, and (3) to work with others to develop and analyze new tissue resources such as those based on collection of pre-invasive lesions and tissue samples and collection of tumor samples associated with clinical trials.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.024 |
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".