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Record W2093541451 · doi:10.1063/1.2437580

Understanding the advantage of remote femtosecond laser-induced breakdown spectroscopy of metallic targets

2007· article· en· W2093541451 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsInstitut National d'OptiqueDefence Research and Development CanadaUniversité Laval
Fundersnot available
KeywordsFilamentationFemtosecondLaser-induced breakdown spectroscopyLaserSupercontinuumMaterials sciencePlasmaSpectroscopyPlasma diagnosticsOpticsOptoelectronicsAtomic physicsPhysicsWavelength

Abstract

fetched live from OpenAlex

We analyze the advantages of remotely sensing metallic targets using femtosecond laser-induced breakdown spectroscopy by studying the temperature and electron density of the plasma ejected from a lead target produced by femtosecond laser pulse filamentation in ambient air. The electron density of 8×1017cm−3 and the plasma temperature of 6794K were obtained for a 20ns time delay with respect to the laser pulse arriving on the target. With these values the signal is high, while the continuum blackbody radiation is low. The continuum emission in the fluorescence spectra is mainly associated with the supercontinuum of the distorted pulse during filamentation (white light laser) in air and this can be controlled. Extrapolation of the single-shot detection limit shows that this technique of filament-induced breakdown spectroscopy could be extended up to the kilometer range, opening up potential applications in metallurgic industry for remote material analysis and process controls.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.250
Teacher spread0.222 · 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