MétaCan
Menu
Back to cohort
Record W2007941129 · doi:10.1088/0957-0233/20/2/025202

Spatial resolution analysis for discrete Fourier transform-based Brillouin optical time domain reflectometry

2008· article· en· W2007941129 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

VenueMeasurement Science and Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsSpectral leakageWindow functionReflectometryImage resolutionDiscrete Fourier transform (general)OpticsFourier transformBrillouin zoneFrequency domainTime domainResolution (logic)Spatial frequencyFast Fourier transformSpectral densityPhysicsMathematicsShort-time Fourier transformFourier analysisComputer scienceAlgorithmTelecommunicationsMathematical analysis

Abstract

fetched live from OpenAlex

Discrete Fourier transform (DFT) requires many sampled points for a spectrum. We find that the spatial resolution of the DFT-based Brillouin optical time domain reflectometry (BOTDR) is determined by the pulse width of the probe light and the time length of the sampling data used to perform the DFT. The best spatial resolution is limited by the pulse width. At a certain sampling rate, the spatial resolution increases linearly with the number of points in DFT. The frequency uncertainty improves with the increased number. Window function restrains the spectral leakage significantly and can improve the spatial resolution. But when the influence of the spectral leakage can be neglected, the frequency uncertainty without a window function is better than that with a window function for the same spatial resolution.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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