Special issue of ADAM devoted to the International Workshop on Symmetries of Graph and Networks 2018
Bibliographic record
Abstract
We are delighted to present this special issue of the Art of Discrete and Applied Mathematics (ADAM), on topics presented or related to topics covered at the TSIMF workshop on 'Symmetries of Graphs and Networks', held at Sanya, on the beautiful semi-tropical island province of Hainan (China), in January 2018.This workshop added to the series of conferences and workshops on symmetries of graphs and networks initiated at BIRS (Canada) in 2008 and progressed in Slovenia every two years from 2010 to 2016.It was attended by 50 mathematicians from China and other parts of the world (including Australia, Canada, New Zealand, Slovakia, Slovenia, South Korea and the USA), many of whom gave lectures on a range of topics involving the symmetries of graphs and maps, including Cayley graphs, arc-transitive graphs and digraphs, covering graphs, regular maps on surfaces, and regular Cayley maps, plus related topics such as graph embeddings and skew morphisms of groups.Participants very much enjoyed the venue, which is similar in style to the BIRS facilities in Banff and Oaxaca and the institute at Oberwolfach, giving plenty of opportunity for interactions between participants, and stimulating further research on the topics covered.This issue contains a number of interesting papers resulting from or associated with the workshop.We would like to thank the authors for their valuable contributions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.126 | 0.043 |
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".