Optimization and Simulation of Kidney Paired Donation Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To my parents and wife for their love ii Acknowledgments My greatest thanks are due to my two advisors, Dr. Jack Kalbfleisch and Dr. Peter Song. On one hand, Dr. Kalbfleisch has deeply influenced my understanding and appreciation of statistics and scientific research in general; to me, he is an incomparable role model. On the other hand, he is simply a very nice Canadian guy named Jack, always accessible and always helpful with great patience. My other advisor, Peter, is an extremely open-minded professor. He is very inspirational to chat with (not limited to research). He cares his students the most, and often puts a student’s deadline before his own. I have received so much more from Peter than I could possibly hope for. Jack and Peter, thank you very much for all of your help and advice throughout my doctoral work. I am grateful to Dr. Alan Leichtman for his constant support. Alan has mentored, encouraged and cared for me for almost three years. His many invaluable suggestions and insights have greatly improved the quality of this dissertation. I would also like to thank Dr. Kerby Shedden for serving on my dissertation committee. He has been very accessible and provided many useful comments on this dissertation. I also want to express my very special thanks to John Swales and Christine Feak from the English Language Institute at the University of Michigan. I could not have written this dissertation without their tremendous help in improving my English writing skills. I am thankful to many friends in Ann Arbor and elsewhere, particularly,
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it