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Record W4393970823 · doi:10.1002/9781119678892.ch8

FIBER OPTIC SENSORS BASED ON THE SAGNAC INTERFEROMETER AND PASSIVE RING RESONATOR

2024· other· en· W4393970823 on OpenAlexaff
Eric Udd

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsDouglas Mental Health University Institute
Fundersnot available
KeywordsInterferometrySagnac effectResonatorOptical fiberOpticsFiber optic sensorRing (chemistry)Fibre optic gyroscopePhysicsMaterials scienceOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

Sagnac interferometers and passive ring resonators can be used to sense a wide range of environmental phenomena. This chapter begins with an introduction to the use of the Sagnac interferometer and passive ring resonator for rotation sensing. This includes an introduction to the Sagnac effect and continues with a discussion of its first commercial implementation in the form of the ring laser gyro. The chapter describes fiber optic gyros in both the Sagnac interferometer and passive ring resonator configurations for open- and closed-loop operation. Optical rotation sensors have made substantial progress in replacing conventional mechanical rotation sensors based on the principle of inertia of spinning masses. In contrast to the ring laser and passive ring resonator gyros that measure the Sagnac effect due to changes in the optical path length of a single circuit, the fiber optic gyro measures the Sagnac effect in a fiber coil having many turns.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.004

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 head0.006
GPT teacher head0.184
Teacher spread0.177 · 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
GenreMethods

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

Citations4
Published2024
Admission routes1
Has abstractyes

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