The 8th International Conference on Highly Frustrated Magnetism (HFM 2016)
Bibliographic record
Abstract
The 8th International Conference on Highly Frustrated Magnetism 2016 (HFM 2016) took place between the 7th and 11th of September 2016 at the GIS Convention Center at National Taiwan University, Taipei, Taiwan. Over 260 participants from all over the world, attended the meeting making it the largest HFM to-date and revealing the impressive growth in the community since the original meeting in Waterloo, Canada where 80 participants attended. Preceding the meeting a school was held at the National Synchrotron Radiation Research Center to help those new to the field understand the material they were likely to see at HFM2016. Our thanks to the international speakers who attended this school John Chalker, Michel Kenzelmann, Philippe Mendels, Luigi Paolasini, Kirrily Rule, Yixi Su, Isao Watanabe and those from Taiwan W. T. Chen, Y-J, Kao, L. J. Chang and C. S. Ku, for their enlightening presentations. The HFM 2016 conference consisted of five plenary talks by H Takagi, B D Gaulin, L Balents, Y Tokura and S T Bramwell, 20 invited and 40 contributed presentations, and about 160 poster presentations from all aspects of theoretical and experimental frustrated magnetism. During the conference period, many stimulating discussions were held both inside and outside the conference room. Excursions to Taipei 101 and the National Palace Museum, as well as several organized dinners and receptions allowed the participants to initiate collaborations and discuss the hottest issues. The subjects covered in the conference included: · Quantum frustrated magnetism and spin liquids · Novel ordering of geometrically frustrated magnets · Frustration effect on the coupling to lattice, orbital and charge degrees of freedom · Exotic phenomena induced by macroscopic degeneracy · Field effect on frustrated magnetism etc. These proceeding represent a very small, but valuable contribution to the community. I hope you enjoy reading them. In view of the rapid growth of the field, it has been decided at the conference that the next HFM conference will be held in two years, in 2018, on the campus of UC Davis, followed one in Shanghai around December 2019. Finally, on behalf of the HFM 2016 Organizing Committee, I wish to deeply thank all the people who generously helped us in organizing and running the HFM 2016 conference. These include the numerous funding sources, Committee Members, Program and Session chairs, Chia-Chi Liu, Chao-Jung Kuo, Hanz Peng, Laura Bravo and Stella Su and of course all the participants, without you it would have simply been another wet week in Taipei. One might remember HFM2016 for the power outage, but we hope you remember the taste, sounds and views of Taipei as well as stimulation conversations and hopefully the beginnings of productive partnerships.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.095 | 0.048 |
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".