All of us are better when we are loved.
Bibliographic record
Abstract
I would very much like to begin by thanking my advisor, Alain Jean Martin, who introduced me to a world without clocks. He has been a wonderful mentor, guiding me with wisdom, patience, humour, and care. This thesis would not have been possible without his inspiration and support, and I shall always appreciate his willingness to make time (even foregoing sleep on Saturday mornings!) to discuss my research. The best of what I learned at Caltech, and what I continue to learn, can be summed up by his lessons: to be intellectually daring, and to strive for elegance in all things. Other professors have been inspiring as well. In particular, I thank André DeHon for his teaching an excellent class at Caltech on electronic design automation, and for his feedback on my thesis and other research papers. I would also like to thank the other professors on my Ph.D. committee, Mani Chandy and Jason Hickey, for their helpful critiques and advice. I am grateful to Jonathan Rose, my undergraduate advisor at the University of Toronto, for introducing me to reconfigurable computing, and for offering suggestions on the asynchronous FPGA research presented here. I also thank Tarek Abdelrahman and Corinna Lee, two professors who both inspired and encouraged me to pursue research in computer hardware when I was an undergraduate student. My years in graduate school have been enriched and enlivened by my fellow students in the
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.072 | 0.098 |
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".