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Record W2894532935 · doi:10.1002/pssa.201800547

Influence of Self‐Trapped Holes on the Responses of Fluorine‐Doped Multimode Optical Fibers Exposed to Low Fluences of Protons

2018· article· en· W2894532935 on OpenAlexaff
Sylvain Girard, Philippe Paillet, Michael Trinzcek, C. Marcandella, A. Alessi, Adriana Morana, Vincenzo De Michele, A. Boukenter, Y. Ouerdane

Bibliographic record

Venuephysica status solidi (a) · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsTRIUMF
Fundersnot available
KeywordsOptical fiberMaterials scienceUltravioletIrradiationDopingFiberMulti-mode optical fiberAttenuationFluorineRadiationInfraredChlorineAnalytical Chemistry (journal)OptoelectronicsOpticsChemistryComposite material

Abstract

fetched live from OpenAlex

The radiation vulnerability of various classes of multimode silica‐based optical fibers is investigated for space applications operating from the ultraviolet up to near‐infrared spectral domains. For this, radiation‐induced attenuation (RIA) levels and kinetics in the 300 ÷ 1100 nm wavelength range are monitored during and after steady state 105 MeV proton exposure at room temperature (RT, equivalent dose of ∼250 Gy(SiO 2 )). The responses of three types of “radiation hardened” optical fibers with either a pure‐silica core (PSC) or fluorine‐doped cores are compared to the one of a Telecom‐grade germanosilicate optical fiber. RIA growth during irradiation and decay after irradiation (recovery phase) reveal that the highly fluorine (2 wt.%)‐doped optical fiber, manufactured by axial vapor deposition process, presents higher RIA levels above 600 nm than other tested optical fibers. Indeed, if the high F‐doping level reduces the UV‐RIA related to SiE’, NBOHC or chlorine‐related centers this composition appears as associated with a strong RIA increase in the visible‐near‐IR through the more efficient generation of RT unstable strain‐assisted or inherent self‐trapped holes.

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 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)0.000
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 score0.006
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.283
Teacher spread0.259 · 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 teacher head, 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

Citations3
Published2018
Admission routes1
Has abstractyes

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