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Disruptive Technology: The Future of SMS Technology

2018· article· en· W2799889363 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.004
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