The Mathematical Legacy of Andrzej Lasota
Bibliographic record
Abstract
Professor Andrzej Lasota (1932–2006) was a Polish mathematician with wide ranging interests in dynamical systems, probability theory and ergodic theory who saw the inter-relationships between all three and who successfully synthesized these apparently disparate fields. He used that synthesis to both further mathematical research as well as to investigate problems in biology. One of his over-riding interests was the way in which seemingly “random” or “probabilistic” processes (in a mathematical sense) could actually be thought of as equivalently coming from deterministic dynamics. How did we each come to know him and his work? Michael C. Mackey met Lasota in Cracow in 1977 through his collaborator Dr. MariaWazewska-Czyzewska, a hematologist and daughter of Professor Tadeusz Wazewski. That meeting blossomed into an almost 30-year-long friendship and collaboration in biomathematics. Marta Tyran-Kaminska met Lasota during her mathematical studies at the University of Silesia in Katowice in 1992 and did her Ph.D. under his supervision. Hans-Otto Walther met Lasota during a year at Michigan State University, 1979–1980, where Pavel Brunovský was also visiting, and they had all been brought together by Shui-Nee Chow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".