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Record W3093526521 · doi:10.37277/stch.v25i2.92

Peningkatan Jumlah Kanal Trafik Menggunakan Common BCCH pada Sistem GSM900 dan DCS1800

2018· article· id· W3093526521 on OpenAlexaff
Rahmilldi Holymonth, Enang Permana S

Bibliographic record

VenueSainstech Jurnal Penelitian dan Pengkajian Sains dan Teknologi · 2018
Typearticle
Languageid
FieldEngineering
TopicEngineering and Technology Innovations
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Abstrak--- Salah satu permasalahan yang dihadapi dalam bidang selular akhir-akhir ini adalah semakin banyaknyapengguna telepon, sehingga membutuhkan kapasitas yang besar dari para operator telekomunikasi. Berbagai risetdan usaha optimalisasi telah dilakukan untuk meningkatkan kapasitas. Common BCCH merupakan salah satu caraoptimalisasi kapasitas tersebut. Common BCCH pada dasanya adalah penggunaan kanal BCCH bersama pada sistemGSM900 dan DCS1800. BCCH yang digunakan hanyalah BCCH yang berasal dari sistem GSM900, sedanganBCCH pada DCS1800 akan diseting sebagai kanal TCH. Sistem ini berhasil dilakukan dan terbukti terjadipeningkatan jumlah kanal TCH dan penurunan bloking

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.010

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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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 designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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