MétaCan
Menu
Back to cohort
Record W2029432098 · doi:10.1117/12.795103

Measuring chromatic dispersion using single-arm interferometers: from millimeters to kilometers

2008· article· en· W2029432098 on OpenAlexaff
Li Qian, Bing Qi, Waleed S. Mohammed, Michael A. Galle, Fei Ye

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAstronomical interferometerDispersion (optics)OpticsPhysicsChromatic scaleInterferometryGeodesyComputer scienceGeology

Abstract

fetched live from OpenAlex

We present three new interferometric techniques for dispersion characterization covering from millimeter waveguides to kilometers of fibers. The first is a Frequency-Shifted Interferometer (FSI) that measures fibers from meters to tens of kilometers. The second is a three-wave Single-Arm Interferometer (SAI), where the envelope of a three-wave interference pattern yields the second-order dispersion directly. It is suitable for fibers from centimeters to >1m. The third is a Common-Path Interferometer (CPI) that measures dispersion of millimeter-long fibers/waveguides. These techniques offer high precision in their respective ranges, and are all "single-arm" interferometers: the two interfering beams go through the same arm of the interferometer. They are simple, low-cost, and more resilient to phase and polarization instabilities than conventional interferometric techniques for dispersion measurement.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.223
Teacher spread0.191 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coherence Tomography ApplicationsFrench-language works237,207