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Record W2071449505 · doi:10.1109/ofc.2007.4348538

Measuring the Optical Signal-to-Noise Ratio in Agile Optical Networks

2007· article· en· W2071449505 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsBirefringencePolarization mode dispersionPolarization (electrochemistry)Optical performance monitoringComputer scienceOptical modulation amplitudeSignal-to-noise ratio (imaging)OpticsSIGNAL (programming language)Electronic engineeringMeasure (data warehouse)Modulation (music)LaserOptical amplifierPhysicsTelecommunicationsOptical fiberWavelength-division multiplexingEngineeringAcoustics

Abstract

fetched live from OpenAlex

We have proposed an improved OSNR measurement technique called optical polarization splitting based on the polarization-nulling method. The test results with Tellabs ultra high speed networks showed that the proposed technique could measure the OSNR accurately at all modulation formats even when the signal was significantly depolarized due to PMD and nonlinear birefringence.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.212
Teacher spread0.197 · 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

Quick stats

Citations13
Published2007
Admission routes1
Has abstractyes

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