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Buried Depressed-Cladding Waveguides Fabricated in RE<sup>3+</sup>:CLNGG Laser Crystals using Direct Laser Writing Technique

2023· article· en· W4386428094 on OpenAlexfundno aff
Gabriela Croitoru, Iulia Anghel, Flavius-Marian Voicu, Madalin Greculeasa, Alin Broasca, Lucian-Marian Gheorghe, N. Pavel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
FundersOntario Ministry of Research, Innovation and Science
KeywordsLaserMaterials scienceFemtosecondOpticsCladding (metalworking)FabricationOptoelectronicsWaveguideCeramicOptical materialsPhysics

Abstract

fetched live from OpenAlex

For at least a decade now, direct writing with a femtosecond (fs) laser beam technique (FS-DLW) is employed for waveguide fabrication in various transparent optical materials [1]. This method makes use of an fs-laser beam to induce changes of the material's optical properties, these modifications being dependent on the medium's nature, on the fs-laser beam parameters, as well as on the focusing conditions [2]. For crystalline and ceramic materials, waveguiding is commonly realized in the volume confined between the written tracks (Type II waveguides), with double-wall or more complex structures. Among YAG and YVO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> crystals, laser gain media with partially disordered crystalline structure have been of great interest because they possess a unique combination of spectral and thermal properties [3–5]. They are a good candidate for generation of short and ultra-short laser pulses from compact structures, using passive Q-switch regime or mode-locking technique.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.266
Teacher spread0.239 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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