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Record W4328019646 · doi:10.22555/pjets.v10i1.700

The Applications, Challenges and Future of LiFi

2022· article· en· W4328019646 on OpenAlexaff
Mumtaz Hissain, Muhammad Saad, Reyaz Ahmed

Bibliographic record

VenuePakistan Journal of Engineering Technology & Science · 2022
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsTrojan Technologies (Canada)
Fundersnot available
KeywordsCloud computingComputer scienceThe InternetTelecommunicationsWirelessInternet of ThingsBandwidth (computing)State (computer science)MultimediaWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

This paper aims to show that Li-Fi now reached a state, where it can be considered a reliable wireless communication solution for future technologies. Currently, the Internet can be accessed using Wi-Fi, but it has its limitations too. It is sufficient for current Internet services but insufficient with IoT and cloud-based services. In the upcoming decade, hard drives will be obsolete and everything will be on the cloud. Modern Web applications i.e., 4K & 8K videos, games, high-resolution photos, etc. require fast bandwidth. Li-Fi opens a reliable door for the end-users to access future technology-based services at high speed.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0060.009
Open science0.0020.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0210.008

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 head0.008
GPT teacher head0.232
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2022
Admission routes1
Has abstractyes

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