MétaCan
Menu
Back to cohort

Progress Towards a Laser Produced Relativistic Electron-Positron Pair Plasma

2016· article· en· W2315527038 on OpenAlexaff
Hui Chen, J. Bonlie, R. Cauble, Frederico Fiúza, W. H. Goldstein, A. Hazi, C. J. Keane, A. Link, E. V. Marley, S. R. Nagel, Jaebum Park, R. Shepherd, G. J. Williams, D. D. Meyerhofer, G. Fiksel, Daniel Barnak, P.-Y. Chang, M. Nakai, Yasunobu Arikawa, H. Azechi, Shinsuke Fujioka, Sadaoki Kojima, N. Miyanaga, T. Morita, Takahiro Nagai, Hitoshi Nishimura, T. Ozaki, Y. Sakawa, H. Takabe, Z. Zhang, S. Kerr, R. Fedosejevs, Y. Sentoku, M. P. Hill, D. J. Hoarty, L. M. R. Hobbs, S. F. James

Bibliographic record

VenueJournal of Physics Conference Series · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsPlasmaLaserPair productionCollimated lightThermal emittancePositronAtomic physicsElectronScalingRelativistic plasmaNuclear physicsAstrophysicsOpticsBeam (structure)

Abstract

fetched live from OpenAlex

A set of experiments has been performed exploring unique characteristics of pair jets and plasmas at several energetic short-pulse laser facilities including Titan at Livermore and OMEGA EP in Rochester, as well as the Osaka LFEX and AWE Orion lasers. New results are summarized, including positron beam emittance, scaling of pair production vs. laser energy, and initial results on the pair jet collimation using electromagnetic fields.

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)0.001
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicLaser-Plasma Interactions and DiagnosticsFrench-language works237,207