Computational Soft Matter: from Synthetic Polymers to Proteins ; NIC Winter School, 29 February - 6 March 2004, Gustav-Stresemann-Institut, Bonn, Germany - Lecture Notes
Bibliographic record
Abstract
The John von Neumann-Institut für Computing (NIC) -a joint institute of Deutsches Elektronen-Synchrotron (DESY) and Forschungszentrum Jülich -supports a large number of research projects in computational science, mainly through the Zentralinstitut für Angewandte Mathematik (ZAM) in Jülich and its supercomputing facilities.Furthermore, NIC also plays an active role in the education of young researchers in the various areas of computational science.It may already be called a tradition that every second year in February/March NIC offers a Winter School about a topic of outstanding methodological importance to the NIC user community.This year the focus of the Winter School is on Computational Soft Matter which has become a very active field of research.Characteristic features of soft matter simulations are the nontrivial geometric structures that occur from the atomistic to the mesoscopic scales, the importance of entropic effects, and the cooperative complex dynamics.Different application fields profit from the recent progress of simulation methods.This preface also offers an opportunity to thank all the individuals and institutions that significantly contributed to the success of the School.First of all we wish to thank all speakers for their written contributions.Without their efforts to generate the extended lecture notes at hand, in spite of the heavy work load they all have to carry, such an excellent reference to the rapidly evolving field of computational soft matter would not have been possible.We also wish to thank the Forschungszentrum Jülich, which this year was the main sponsor of the School.For their most valuable help with the local arrangements we are greatly indebted to several staff members of the Forschungszentrum Jülich, namely Rüdiger Esser (finance), Rene Gail (conference service), and last but not least the School's secretaries Anke Reinartz and Yasmin Abdel-Fattah.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".