CAS course on Power Converters in Baden, Switzerland
Bibliographic record
Abstract
The CERN Accelerator School (CAS) and the Paul Scherrer Institute (PSI) recently organised a specialised course on Power Converters, which was held at the Hotel du Parc in Baden, Switzerland from 7 to 14 May 2014. Photo courtesy of Markus Fischer, Paul Scherrer Institut. Following some recapitulation lectures on accelerators and the requirements on power converters, the course covered a wide range of topics related to the different types of power converters needed for particle accelerators. Topical seminars completed the programme. The course was very successful, attended by 84 students representing 21 nationalities, mostly from European countries but also from America, Brazil, Canada, China, Iran, Jordan and Thailand. Feedback from the participants was very positive, reflecting the high standard of the lectures and teaching. In addition to the academic programme, the participants also had an opportunity to take part in a full-day site visit to ABB and PSI and an excursion to the Rhine Falls. Sponsoring in the form of scholarships was offered by CAENels, OCEM and CERN to deserving students who would otherwise not have been able to attend. Forthcoming CAS courses will be a specialised school on Plasma Wake Acceleration to be held at CERN, Geneva, Switzerland from 23 to 29 November 2014 and a US-CERN-JAPAN-RUSSIA Joint International Accelerator School course on Beam Loss and Accelerator Protection to be held in Newport Beach, California, USA from 5 to 14 November 2014. More information on both of these schools is available on the CAS website.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.495 | 0.243 |
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".