Bibliographic record
Abstract
listening to me, answering my questions, and for carefully reading this manuscript. He made my stay in Waterloo very educational. I thank all members of the MAVERIC research group for some interesting discussions on designing up-down counters. In one of those discussions Peter Mayo gave me the idea for an up-down counter with constant power consumption. There are more people at the Computer Science Department of the University of Waterloo that deserve to be acknowledged. I will not try to mention all of them, because I would undoubtedly forget someone. Hereby I thank all of them. The International Council for Canadian Studies is acknowledged for their financial support. The Government of Canada Award that they awarded to me made my stay in Waterloo financially possible. I thank Rudolf Mak for getting me in touch with Jo Ebergen, and Franka van Neerven for helping with all kinds of organizational details that had to be taken care of before I could go to Waterloo. Finally, I thank Twan Basten for being patient with me and listening to me during the stay in Waterloo.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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 teacher head, 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".