Bibliographic record
Abstract
Alán Aspuru-Guzik’s nearly 2 decades of research can’t be narrowed down to a single scientific field. But the flurry of ideas coming from his lab fits, if precariously, under a grander theme: expansion. “I’m very interested in exploring life,” he says. “When there are opportunities that expand my horizon, I will take them.” For Aspuru-Guzik, a professor of chemistry and computer science at the University of Toronto, the possibilities of the unknown are what have driven his laboratory to the interfaces of quantum computing, machine learning, and chemistry. Aspuru-Guzik blurs the lines between everything: theoretical and experimental chemistry, art and science, and creativity and stiffness. “For Alán, disciplines are artificial,” says Joel Yuen-Zhou, who received his PhD from Aspuru- Guzik’s lab in 2012 and now serves as an associate professor of chemistry at the University of California San Diego. Aspuru-Guzik’s interests span art, literature, music, and science, which made him
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".