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Record W2037812196 · doi:10.1117/12.778943

Performance characterization of PON technologies

2007· article· en· W2037812196 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsCommunications Research Centre Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceElectronic engineeringAttenuationLink budgetBit error rateDispersion (optics)TelecommunicationsOpticsEngineeringDecoding methodsPhysics

Abstract

fetched live from OpenAlex

The simulation models for a typical PON layout are developed and three major PON technologies are considered. The models support the analysis of various important characteristic parameters, namely: 1) link budget for acceptable losses from splices, attenuation and splitters, 2) link performance characterization based on data (BER, SNR) or video signal quality, and 3) linear and nonlinear fiber effects such as dispersion, PMD, self- and crossmodulation, FWM, etc. Analysis outcomes may be used to optimize the performance of the applied system design including fiber maximum length and type, the need to change some of the optical components (e.g. couplers, splitters, etc.) and digital links bit rate (e.g. 1.2 Gb/s or 2.4 Gb/s) according to the required BER. The simulation models developed enable us with these detailed analyses of PON technologies without the need to build prototypes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.224
Teacher spread0.214 · 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