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Investigation of High Temperature Baking of Jacketed Quarter Wave Resonators

2017· article· en· W2945253395 on OpenAlex

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJACOW · 2017
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceQuarter (Canadian coin)Composite materialHistory

Abstract

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The Superconducting booster Linac at IUAC has been delivering accelerated beams for scheduled experiments since 2013. It has three accelerating modules with 8 Quarter Wave Resonators (QWR)in each. The QWRs for the first module were built at Argonne National Laboratory while those for the second and third modules have been built in-house. During the electropolishing of one of the indigenously built resonators (QWR # I03) the RF surface got spoiled due to a wrong acid mixture that was being used for etching. In subsequent cold tests of the cavity, its performance was poor (2.6 MV/m @ 4W). There was evidence of Q disease also, as the performance deteriorated further (~20%) when the cavity was held at 100-120K for ~8 hours .In an attempt to recover the cavity it was baked at 650 °C for 10 hours along with its stainless steel jacket. A series of tests were conducted thereafter wherein, a substantial improvement (factor of two) in the performance was observed. Encouraged with the results another QWR designed for a lower beta (β=0.05) was also heat treated identically. This paper presents the different treatments followed to enhance the cavity performance vis-à-vis the test results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.218
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it