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Record W3193989621 · doi:10.1139/facets-2021-0011

The need for speed

2021· article· en· W3193989621 on OpenAlexaffvenue
Jean-Claude Kieffer

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsFemtosecondIsochoric processLaserPlasmaMaterials scienceEnergy densityPower densityFOIL methodEngineering physicsOptoelectronicsNanotechnologyNuclear engineeringPower (physics)PhysicsOpticsNuclear physicsEngineering

Abstract

fetched live from OpenAlex

This paper reviews the challenges posed by the physics of the interaction of high-peak power femtosecond lasers with ultrathin foil targets. Initially designed to produce warm solid-density plasmas through the isochoric heating of solid matter, the interaction of an ultrashort pulse with ultrathin foils is becoming more and more complex as the laser intensity is increased. The dream of achieving very hot solid density matter with extreme specific energy density faces several bottlenecks discussed here as related to the laser technology, to the complexity of the physical processes, and to the limits of our current time-resolved instrumentations.

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)0.004
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.086
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0100.028
Open science0.0030.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0860.046

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 head0.015
GPT teacher head0.289
Teacher spread0.274 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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