MétaCan
Menu
Back to cohort
Record W6947957914 · doi:10.48550/arxiv.2010.02192

Coaxial multi-mode cavities for fundamental SRF research in an unprecedented parameter space

2020· preprint· en· W6947957914 on OpenAlexaff

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
Fundersnot available
KeywordsCoaxialMode (computer interface)Superconducting radio frequencyRadio frequencySurface (topology)Characterization (materials science)Fundamental frequency

Abstract

fetched live from OpenAlex

Recent developments in superconducting radio-frequency (SRF) research have focused primarily on high frequency elliptical cavities for electron accelerators. Advances have been made in both reducing RF surface resistance and pushing the readily achievable accelerating gradient by using novel SRF cavity treatments including surface processing, custom heat treatments, and flux expulsion. Despite the global demand for SRF based hadron accelerators, the advancement of TEM mode cavities has lagged behind. To address this, two purpose-built research cavities, one quarter-wave and one half-wave resonator, have been designed and built to allow characterization of TEM-mode cavities with standard and novel surface treatments. The cavities are intended as the TEM mode equivalent to the 1.3GHz single cell cavity, which is the essential tool for high frequency cavity research. Given their coaxial structure, the cavities allow testing at the fundamental mode and higher harmonics, giving unique insight into the role of RF frequency on fundamental loss mechanisms from intrinsic and extrinsic sources. In this paper, the cavities and testing infrastructure are described and the first performance measurements of both cavities are presented.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.011
Open science0.0060.009
Research integrity0.0000.001
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 head0.551
GPT teacher head0.379
Teacher spread0.172 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicResearch Data Management PracticesFrench-language works237,207