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Record W2762701733 · doi:10.1109/ihtc.2017.8058207

A novel method for assessing broadband state at K-12 schools

2017· article· en· W2762701733 on OpenAlexaff
Mirza Kamaludeen, Salam Ismaeel, Carm Scarfo, Soussan Tabari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceBroadbandArtificial neural networkBroadband networksState (computer science)Index (typography)Network architectureArchitectureData miningMachine learningComputer networkTelecommunicationsAlgorithmWorld Wide Web

Abstract

fetched live from OpenAlex

This work presents a useful standard management tool called Broadband Condition Index (BCI), which measures WAN network condition. It is a functional indicator resulting from an analysis of different unrelated factors (such as current WAN architecture and number of users) to obtain an overview of WAN condition as a numerical value. The proposed BCI has been developed based on “knowledge-based” approach to planning and conducting many factors in real data come from more than 5000 schools and 72 District School Boards (DSBs). This knowledge was used to build a rule base system which help to estimate the training data for a Feed-Forward Neural Network (FFNN). The proposed BCI has been tested on real instances and gave the same percentage of expected results.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.768
Threshold uncertainty score0.423

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 science0.0000.000
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 head0.059
GPT teacher head0.366
Teacher spread0.307 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2017
Admission routes1
Has abstractyes

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