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Record W2065866951 · doi:10.1364/ofs.2006.the88

Identification of Damage on Optical Ground Wire Cable Using Distributed Brillouin Fiber Sensor

2006· article· en· W2065866951 on OpenAlexaff
Lufan Zou, Xiaoyi Bao, Fabien Ravet, Liang Chen

Bibliographic record

VenueOptical Fiber Sensors · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceBrillouin zoneOptical fiberOptical fiber cableFiber optic sensorFiberElongationStress (linguistics)Composite materialLayer (electronics)Strain (injury)OpticsBrillouin scatteringPhysicsUltimate tensile strength

Abstract

fetched live from OpenAlex

A distributed Brillouin fiber sensor with centimeter spatial resolution has been employed for the measurement of small crack up to 1.5 cm. The out-layer damaged regions on an optical ground wire (OPGW) cable have been identified successfully by measuring the strain distributions every 5 cm. The stress increased to 127 kN which corresponds to more than 7000 micro-strain in the fibers. The locations of structural indentations comprising repaired and undamaged regions are found and distinguished using their corresponding strain data. The elongation of repaired region increases with time under 127 kN. These results are quantified in terms of the fiber orientation, stress, and behavior relative to undamaged sections.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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