Operations Research Techniques in Constraint Programming
Bibliographic record
Abstract
The past four and a half years I have been a PhD student at the CWI in Amsterdam, with great pleasure.I have carried out my research in the group PNA1, which contains researchers from operations research as well as constraint programming.This turned out to be a fertile basis for my investigations on the combination of the two fields.My supervisor, Krzysztof Apt, was working on a somewhat different research topic.To illustrate this, we have not written a single joint paper in all those years, although this is probably also due to his ethical views on science.As a result, I have performed my research rather independently, which I have appreciated very much.Nevertheless, Krzysztof has supported and advised me in many different ways.I want to thank him for his guidance and, perhaps as important, confidence.Krzysztof realized that a background in constraint programming and operations research alone is not necessarily sufficient to perform interesting research on their combination.Hence, I was sent to Bologna, to collaborate with an expert in the field: Michela Milano.I have visited Bologna several times, and these visits turned out to be very fruitful.Apart from the papers we have written together, Michela has also acted as a mentor in the research process.I believe that her influence has been very important for my development.For all this I am very grateful to her.Another pleasant aspect of my visits to Bologna has been the "working environment".I am very thankful to Andrea Lodi, Andrea Roli, Paolo Torroni, and all other members of the Drunk Brain Band for their hospitality, company, and (in some cases) interesting discussions about research issues.During the past year I have also successfully collaborated with Gilles Pesant and Louis-Martin Rousseau.I want to thank them for this experience and for hosting me for a month in Montreal.Further, I am thankful to Eric Monfroy for interesting discussions and a very enjoyable visit to Nantes.Many of the above visits would not have been possible without the support of the CWI, which I have very much appreciated.At the CWI, I have benefited enormously from the seminars and the knowledge of
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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 | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".