9th International Particle Accelerator Conference, IPAC18
Bibliographic record
Abstract
Preface Introduction The ninth International Particle Accelerator Conference, IPAC’18, took place at the J.W. Marriott Parq Hotel, Vancouver, British Columbia, Canada from Sunday to Friday, April 29 to May 04, 2018. IPAC’18 was attended by 1,276 delegates from 31 countries on all continents. The tally includes 125 industry delegates, but excludes the 88 exhibitor registrations. Hosted by the TRIUMF Laboratory, the conference was organized under the auspices of the Institute of Electrical and Electronics Engineers (IEEE), and the American Physical Society Division of Physics of Beams (APS‐DPB). Established in 1968 in Vancouver, TRIUMF is Canada’s particle accelerator centre. Delegates and Exhibitors were supported by a 26‐ member Local Organizing Committee (LOC) volunteered by TRIUMF. 214 young scientists from all over the globe attended the conference. 99 of these students received travel grants thanks to the sponsorship of societies, institutes and laboratories worldwide. The Americas region sponsors are: APS, NSF and TRIUMF. The Asia region sponsors are: ANSTO, IHEP, RIKEN, KEK, SSRF and PAL. The Europe region sponsors are: CEA, CELLS, CERN, Cockcroft, DESY, Diamond, ELETTRA, ESRF, ESS, GANIL, GSI, HZB, INFN, in2p3, KIT, MAXIV, PIS, SOLEIL, STFC. The IPAC’18 budget contributed $20,000 student grants to each region. The organizers of IPAC’18 are grateful to all sponsors for their valued support of students.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.465 | 0.371 |
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".