MétaCan
Menu
Back to cohort
Record W2102799228 · doi:10.5539/nct.v1n2p1

Modeling Latency in a Network Distribution

2012· article· en· W2102799228 on OpenAlexvenueno aff
Rohitha Goonatilake, Rafic Bachnak

Bibliographic record

VenueNetwork and Communication Technologies · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLatency (audio)Computer scienceReliability (semiconductor)Transmission (telecommunications)Computer networkData transmissionReal-time computingRegression analysisTelecommunicationsMachine learning

Abstract

fetched live from OpenAlex

Network latency causes a delay in transmitting a message from one location to another. This can be attributed to several other factors, such as network congestion, network traffic, and computer storage capacities. Of course, the distance between two locations is the main factor that contributes to the delay. Since transmission between two cities will not be a straight path, latency is subject to detour and can be a factor of any deviation between these cities. These factors, along with a loss of the data and energy aspects of the transmission, will be investigated as this paper attempts to summarize latency estimation using regression and numerical models. Path prediction can be done up to a number of transmission towers or satellites between two cities. Latency estimation to locate either the client, client server, or host will be analyzed using a liner regression model leading to the same numerical model. Reliability analysis stemming from latency will be done at the end of this article.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueNetwork and Communication TechnologiesSame topicComplex Network Analysis TechniquesFrench-language works237,207