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Record W6931551952 · doi:10.5281/zenodo.5016578

XLS - D3.1: Preliminary assessments and evaluations of the optimum e-gun and injector solution for the CompactLight design

2019· article· en· W6931551952 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsCanadian Nautical Research Society
FundersEuropean Commission
KeywordsInjectorLinear particle acceleratorRepetition (rhetorical device)Beam (structure)Range (aeronautics)Radio frequency

Abstract

fetched live from OpenAlex

In this deliverable we report an overview of the possible injector options suitable to match the CompactLight X-band high brightness linac able to drive short wavelength FELs user facilities. Different schemes have been investigated including RF gun injectors at different operating frequency (S, C and X band) and a DC gun based design. The electromagnetic and RF designs for all cases are reported and discussed, including a preliminary evaluation of the laser/cathode system requirements. Matchings with the downstream linac are also investigated with beam dynamics simulations. State of the art S-band injectors look appropriate to achieve the required parameters at low repetition rate ( 100 Hz). On the other end a compact C-band (or X-band) RF gun design is expected to have even better performances at low repetition rates and moreover, due to the lower thermal load, could allow higher repetition rates operation, up to 1 kHz, with acceptable performances degradation. DC guns are also very promising solutions for kHz range operation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.002

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 head0.069
GPT teacher head0.311
Teacher spread0.242 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicSalivary Gland Disorders and FunctionsFrench-language works237,207