Bibliographic record
Abstract
The 12th edition of the conference GASCom on random and exhaustive generation of combinatorial structures was held in Varese, Italy, on June 13-15th, 2022.The very first edition of the series of conference GASCom took place in Bordeaux in 1994, followed by subsequent editions in Caen (1997), in Bordeaux (1999) and in Siena (2001).Then, starting in 2006, the conference has been held regularly every two years until now (with the only exception of the 2020 edition, due to the Coronavirus pandemic), specifically in Dijon (2006), Bibbiena (2008), Montreal (2010), Bordeaux (2012), Bertinoro (2014), Bastia (2016), Athens (2018) and of course Varese (2022).This special issue on Randomness and Combinatorics contains a selection of papers presented at the 2022 conference and additional papers from this area of research.Starting from the common theme of random and exhaustive generation of combinatorial objects, the topics explored in the collected contributions span from all kinds of enumerative, bijective and analytic combinatorics, to algorithmic aspects (such as analysis of algorithms and probabilistic algorithms) and interactions with other areas of mathematics and computer science (such as combinatorics on words and tilings).The selection of papers we have made would not have been possible without the help of several referees, whose invaluable efforts have been fundamental in making all final decisions.We heartfully thank all of them for having made this volume possible.We would like to thank all the participants at GASCom 2022 and the authors of the collected papers, whose precious contributions have been the main ingredients for a successful conference and an equally successful special issue.In particular, we would like to express our gratitude to the three invited speakers Cyril Banderier (LIPN, Paris, France), Paola Bonizzoni (University of Milan-Bicocca, Italy) and Tony Guttmann (University of Melbourne, Australia), whose insightful lectures have been an inspiration to all members of the GASCom community.In closing this preface, our final thought goes to one of the founders of GASCom, Jean-Guy Penaud, who passed away a few months before the conference.His untimely death deeply saddened all members of the scientific community and all friends of GASCom.His scientific expertise and friendliness will be deeply missed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.541 | 0.377 |
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".