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Record W2796776125 · doi:10.25105/jetri.v13i2.497

ANALISIS PENAMBAHAN LARUTAN BENTONIT DAN GARAM UNTUK MEMPERBAIKI TAHANAN PENTANAHAN ELEKTRODA PLAT BAJA DAN BATANG

2016· article· id· W2796776125 on OpenAlexaff
Ishak Kasim, David Hana Hertog, Dean Corio

Bibliographic record

VenueJETri Jurnal Ilmiah Teknik Elektro · 2016
Typearticle
Languageid
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsOhmNuclear chemistryChemistry

Abstract

fetched live from OpenAlex

Grounding system is required in order to protect a building or electronic equipment from damage caused by exposed lightning strikes. The maximum value of grounding resistance for a building is 5 ohm (PUIL 2000) and 3 ohm for electronic equipment (PT Telkom, 1994). A good grounding system is achiered by minimizing the value of the grounding resistance and it is done by giving an additives solution to the ground. The additive solution in this research is the mixture of bentonite and salt solution. After the additive solution was given, it shows the grounding resistance decrease to less then 1 ohm.Sistem pentanahan diperlukan untuk mengamankan suatu gedung atau peralatan elektronik yang ada di dalamnya agar tidak mengalami kerusakan akibat terkena sambaran petir. Nilai tahanan pentanahan maksimum untuk sebuah gedung adalah 5 ohm (PUIL 2000) dan 3 ohm untuk peralatan (PT Telkom, 1994). Perlu dilakukan perencanaan sistem pentanahan yang baik untuk memperkecil nilai tahanan pentanahan yang dapat dilakukan dengan penambahan zat aditif pada tanah. Pada penelitian ini zat aditif berupa larutan garam dan larutan bentonit. Pengukuran yang dilakukan setelah penambahan larutan garam dan bentonit menunjukan penurunan nilai tahanan pentanahan menjadi lebih kecil dari 1 ohm.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2016
Admission routes1
Has abstractyes

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