MétaCan
Menu
Back to cohort
Record W2017177774 · doi:10.1002/nem.705

End‐to‐end DWDM optical link power‐control via a Stackelberg revenue‐maximizing model

2008· article· en· W2017177774 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

VenueInternational Journal of Network Management · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStackelberg competitionComputer scienceMathematical optimizationFlexibility (engineering)Profit (economics)Power (physics)Coordinate descentRevenuePower controlAlgorithmMathematical economicsMathematics

Abstract

fetched live from OpenAlex

Abstract This paper deals with a Stackelberg formulation for the power control problem in optical networks. The new model adds an extra dimension to the recent OSNR game model and gives flexibility in optimizing the network performance (e.g. OSNR) and regulate the network conditions (e.g. power capacity). We ‘engineer’ the Stackelberg player to be a revenue‐maximizing agent who designs pricing policies with complete information of the followers to optimize his own profit. We investigate both the unconstrained Stackelberg model and the one subject to capacity constraints, and characterize their solutions analytically. Finally, we use geometric programming and coordinate descent method to design a distributed and iterative algorithm that is suited to the Stackelberg model implementation in optical networks. Copyright © 2008 John Wiley & Sons, Ltd.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.233
Teacher spread0.222 · 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