Bibliographic record
Abstract
A little more than a quarter-century ago, I received an inquiry from a young Assistant Professor of Applied Mathematics at the University of Tulsa, the honoree of this volume, Carlos Castillo-Chavez. Though he was well situated in a faculty job, he was not satisfied: He was interested in mathematical biology, having written an excellent thesis in population biology with Fred Brauer at Wisconsin entitled Linear and Nonlinear Deterministic Character-Dependent Models with Time Delay in Population Dynamics. But that success had only whetted his appetite to become more deeply embedded in biology, and he was prepared to give up his faculty job to start a postdoctoral fellowship in ecology. It is always difficult to read in such letters what potential exists in the author; but there was something about what Carlos wrote, the obvious sacrifice he was prepared to make, and my regard for Fred Brauer that convinced me that I must meet this fellow. We did meet, for lunch in an LA restaurant, and the qualities that have led to his remarkable career were immediately obvious. I resolved on the spot to make sure he joined our group. Carlos arrived at Cornell shortly thereafter, and did not leave for nearly twenty years.For more information please click the “Full Text” above.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 0.091 |
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".