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Record W4404562766 · doi:10.1109/jsen.2024.3498459

A High Spatial Resolution Multipoint Optical Fiber Temperature Sensor With an Interlaced Sheath—Design, Analysis, and Experimental Validation

2024· article· en· W4404562766 on OpenAlexafffund
Yongqiang Deng, Jin Jiang

Bibliographic record

VenueIEEE Sensors Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsWestern University
FundersUniversity Network of Excellence in Nuclear EngineeringNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsOptical fiberTemperature measurementMaterials scienceImage resolutionFiber optic sensorPoint (geometry)Resolution (logic)FiberOpticsOptoelectronicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

This article presents design, analysis, and experimental validation of a novel multipoint optical fiber sensor (OFS) probe of high spatial resolution. The probe incorporates an innovative interlaced thermal conductor-insulator-conductor (CIC) architecture to mitigate effects of thermal smearing that often occurred with the sheath in existing probes. This design ensures minimal thermal resistance between the sensor elements and the measurement environment. The design is backed by a detailed thermal analysis and computer simulation. Three prototype probes are constructed and tested in realistic environments to validate the design concept and evaluate the probe performance in transient and steady-state conditions. The results have confirmed unequivocally the unique features of this design and superior performance in achieving high spatial resolution in simultaneous measurements of localized temperatures in nonuniformly distributed thermal fields.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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