Bibliographic record
Abstract
I was fortunate enough to start my career at what was the dawn of modern-day molecular biology and to apply it to an important health problem. While my early work focused on fundamental science, the desire to understand human disease better and to find practical applications for research discoveries resulted, over the following decades, in creating a stream of translational research directed specifically toward epithelial cancers. This could only have been possible through multiple collaborations. This type of team science would eventually become a hallmark of my career. With the development of higher throughput molecular techniques, the pace of research and discovery has quickened, and the concept of personalized medicine based on genomics is now coming to fruition. I hope my legacy will not just reflect my published works, but will also include the impact I have had on the development of the next generation of scientists and clinician scientists who inspired me with their dedication, knowledge, and enthusiasm.
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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.030 | 0.035 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.012 | 0.046 |
| Insufficient payload (model declined to judge) | 0.039 | 0.033 |
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".