MétaCan
Menu
Back to cohort
Record W2073672303 · doi:10.1063/1.4892985

Photoluminescence mapping of oxygen-defect emission for nanoscale spatial characterization of fiber Bragg gratings

2014· article· en· W2073672303 on OpenAlexafffund
David Barba, F. Martín, Kamel Tagziria, Mischa Nicklaus, E. Haddad, Federico Rosei, Andreas Ruëdiger

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPhotoluminescenceMaterials scienceFiber Bragg gratingRefractive indexOpticsOptoelectronicsCore (optical fiber)Optical fiberWavelengthFiberCharacterization (materials science)NanotechnologyComposite material

Abstract

fetched live from OpenAlex

Confocal photoluminescence (PL) microscopy is used to gain insight into the inner structure of Ge-doped Fiber Bragg Gratings (FBGs). These measurements pinpoint room temperature PL emission from oxygen-related defects in the visible range, whose spatial distribution exhibits a periodicity associated with the spatial modulation of the refractive index printed inside the fiber core of the FBG. The period measured by PL mapping performed at submicrometric resolution matches the period of the refractive index variation determined from the optical transmission wavelength using the Bragg condition. Since the PL emission of oxygen-related defects can be used to probe local chemical changes inside fused silica, this novel and non-destructive experimental approach can be implemented for the direct characterization of FBGs, to study the effects of gas conditioning, ageing, and degradation under various environments.

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 score0.219
Threshold uncertainty score0.504

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 studies0.0000.000
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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicAdvanced Fiber Optic SensorsFrench-language works237,207