Bibliographic record
Abstract
A million thanks to Klaus Jansen, my supervisor, for his invaluable guidance. It was him who introduced me to the field and community of combinatorial opti-mization and approximation algorithms. Only with his inspiration, his advice and his patience was I able to start my research career. This thesis has gained much from his co-authorship in the corresponding papers. I am very grateful to him for his support, help and friendship. Many thanks are due to Anand Srivastav, Denis Trystram and Roberto Solis-Oba, for their kind help to me in both my work and my career. Their support, concerning and encouragement has benefited my research and my future much. I wish to thank Denis for his providing me the opportunity of working in Grenoble. I thank Tamás Terlaky, for his offer of the postdoctoral fellowship at the McMaster University. I also thank Igor Averbakh for his kind offer at the University of Toronto. I am very grateful to Jana Chlebíková, Aleksei Fishkin, Olga Gerber, Ute Iaquinto,
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 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.273 | 0.169 |
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".