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Record W2469896409

High-power beam injectors for 100 KW free-electron lasers

2003· article· en· W2469896409 on OpenAlexaff
A.M.M. Todd, Richard L. Wood, H Bluem, M. Wiseman, D. Schrage, C. Rode, Robert Rimmer, Dinh C. Nguyen, J P Kelley, Sergey Kurennoy, R.L. Wood

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2003
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsDiscovery Air (Canada)
FundersLos Alamos National LaboratoryU.S. Department of Energy
KeywordsInjectorLaserPower (physics)Cathode rayMaterials scienceBeam (structure)OptoelectronicsLaser beamsOpticsElectronPhysicsNuclear physics
DOInot available

Abstract

fetched live from OpenAlex

A key technology issue on the path to high-power FEL operation is the demonstration of reliable, high-brightness, high-power injector operation. We describe two ongoing programs to produce 100 mA injectors as drivers for 100 kW free-electron lasers. In one approach, in collaboration with the Thomas Jefferson National Accelerator Facility, we are fabricating a 750 MHz superconducting RF cryomodule that will be integrated with a room-temperature DC photocathode gun and tested at the Laboratory. In the other approach, in collaboration with Los Alamos National Laboratory, a high-current 700 MHz, normal-conducting, RF photoinjector is being designed and will undergo thermal management testing at the Laboratory. We describe the design, the projected performance and the status of both injectors.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.006
GPT teacher head0.156
Teacher spread0.150 · 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 designBench or experimental
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
Published2003
Admission routes1
Has abstractyes

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