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Record W2158332635 · doi:10.1109/tcad.2010.2046810

Sensitivity Analysis of Lossy Multiconductor Transmission Lines Based on the Passive Method of Characteristics Macromodel

2010· article· en· W2158332635 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

VenueIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsWestern University
Fundersnot available
KeywordsLossy compressionSensitivity (control systems)Computer scienceNonlinear systemElectronic engineeringElectric power transmissionFrequency domainTransmission (telecommunications)Time domainFeature (linguistics)AlgorithmEngineeringTelecommunicationsElectrical engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper describes an efficient approach for time domain sensitivity analysis of lossy high speed interconnects in the presence of nonlinear terminations. The sensitivity information is derived using the recently developed passive method of characteristics. An important feature of the proposed method is that the sensitivities are obtained from the solution of the original network, leading to significant computational advantages. Numerical examples are presented to illustrate the validity of the proposed approach.

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 score0.686
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 spread0.224 · 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