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Record W2982560640 · doi:10.51179/vrs.v10i5.708

PENGARUH SULFAT DALAM AIR TANAH SEBAGAI AIR CAMPURAN TERHADAP KUAT TEKAN BETON DENGAN NILAI FAS 0,65

2018· article· id· W2982560640 on OpenAlexaff
R. Dedi Iman Kurnia, Syarizal Fhonna, Syifaul Husni, Suhaimi Suhaimi

Bibliographic record

VenueVariasi /Variasi · 2018
Typearticle
Languageid
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsEnvironmental scienceAnimal science

Abstract

fetched live from OpenAlex

Kuat tekan beton dipengaruhi oleh unsur-unsur pembentuk beton, salah satunya adalah air yang berfungsi sebagai pemicu proses kimiawi semen sebagai bahan perekat dan melumasi agregat supaya mudah dikerjakan. Proses kimiawi semen dan kuat tekan beton sangat tergantung dari kandungan yang terdapat dalam air campuran. Penelitian ini dilakukan untuk melihat pengaruh kandungan sulfat dalam air tanah yang digunakan untuk campuran beton terhadap kuat tekan. Metode penelitian ini adalah eksperimental, yaitu mengambil beberapa sampel air tanah yang terdapat di wilayah pesisir Kabupaten Bireuen untuk diuji kandungan kimianya. Air tanah tersebut kemudian digunakan untuk campuran beton dengan faktor air semen (FAS) 0,65. Uji kuat tekan beton dilakukan pada umur 3, 7, 14 dan 28 hari. Hasil penelitian menunjukkan bahwa ion sulfat yang terkandung dalam air campuran beton berpengaruh negatif terhadap kuat tekan beton. Sehingga, semakin besar kadar kandungan sulfat dalam air campuran maka kuat tekan beton semakin kecil. Air campuran beton yang mengandung ion sulfat juga memperlambat proses hidrasi dan perkerasan beton sesuai dengan konsentrasi kadar kandungan sulfatnya.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.208
Teacher spread0.198 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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