MétaCan
Menu
Back to cohort

Disruptive Technology: The Future of SMS Technology

2018· article· en· W2799889363 on OpenAlexaboutno aff
Cik Ku Haroswati Binti Che Ku Yahaya, Murizah Kassim, H Husni

Bibliographic record

VenueIndonesian Journal of Electrical Engineering and Computer Science · 2018
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsShort Message ServiceService (business)SMS bankingQuarter (Canadian coin)TelecommunicationsAdvertisingComputer scienceBusinessInternet privacyWorld Wide WebMarketingGeography

Abstract

fetched live from OpenAlex

The research illustrates a view on the current trends in the telecommunication industry focusing on the matter of Short Message Service (SMS) technology. It targets and explains disruptive technology and also introduces disruptive technology in Short Message Service (SMS). The methodology of this research is market trend analysis using data on volume of Short Message Service (SMS) sent and received through a mobile network and a survey that was conducted with questionnaires. The findings are Short Message Service (SMS) is predicted to become obsolete and be disrupted by Over the Top (OTT) messaging application by the third quarter of the year 2020. This was implemented with linear regression prediction. By the survey a new trend of using Short Message Service (SMS) has emerged but users have certainly moved away from SMS and now prefer texting Over the Top (OTT) messaging applications.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0070.011
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.004
GPT teacher head0.209
Teacher spread0.204 · 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
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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueIndonesian Journal of Electrical Engineering and Computer ScienceSame topicICT in Developing CommunitiesFrench-language works237,207