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Record W3005640959 · doi:10.21307/ijssis-2019-061

Effect of TiO <sub>2</sub> nano-overlays deposited with atomic layer deposition on refractive index sensitivity of long-period gratings

2014· article· en· W3005640959 on OpenAlexaff
Krzysztof Krogulski, Mateusz Śmietana, Bartosz Michalak, Anna K. Dębowska, M. Szymańska, x ukasz Wachnicki, Sylwia Gierałtowska, M. Godlewski, Predrag Mikulic, Wojtek J. Bock

Bibliographic record

VenueInternational Journal on Smart Sensing and Intelligent Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsAtomic layer depositionMaterials scienceRefractive indexDeposition (geology)Layer (electronics)Thin filmNano-CoatingOpticsHigh-refractive-index polymerOptoelectronicsSensitivity (control systems)OverlayNanotechnologyElectronic engineeringComposite materialComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract This paper we discusses an effect of coating long-period gratings (LPGs) with a thin titanium dioxide (TiO 2 ) overlays on refractive index (RI) sensitivity of the LPG. The overlays have been obtained using atomic layer deposition (ALD) method. The method allows for deposition well-controlled in thickness, well defined in optical properties, high-RI and very conformal thin films as required for optical fiber sensors. For the investigated LPGs we obtained sensitivity of 3490 to 6471 nm per RI unit depending on range RI.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.222
Teacher spread0.216 · 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
Published2014
Admission routes1
Has abstractyes

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