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Record W2983005376 · doi:10.25105/psia.v1i1.5983

ANALISIS PENURUNAN TANAH LUNAK AKIBAT PENIMBUNAN BERTAHAP

2019· article· id· W2983005376 on OpenAlexaff
Yudha Aditya Pradita

Bibliographic record

VenueProsiding Seminar Intelektual Muda · 2019
Typearticle
Languageid
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Pada suatu konstruksi diperlukan tanah pondasi yang cukup kuat untuk menahan beban yang nantinya diberikan di atasnya. Pada tanah lunak, konstruksi sangat sulit dilakukan karena tanah lunak karena memiliki daya dukung yang rendah dan penurunan yang cukup besar juga waktu penurunan maksimum yang sangat lama keran permeabilitasnya yang rendah. Maka dari itu, diperlukan sebuah metode untuk dilakukan perbaikan pada tanah jenis ini. Metode perbaikan tanah yang dipakai ialah penimbunan tanah secara bertahap, sehingga layak dan memenuhi persyaratan sebagai lapisan pondasi. Penimbunan bertahap dapat dilakukan dengan memberikan pembebanan berupa tanah yang dirasa cukup baik untuk dijadikan tanah timbunan, untuk memperkuat tanah yang akan ditimbun. Berdasarkan hasil analisis yang dilakukan, dihasilkan nilai kuat geser tanah ( ) yang semakin tinggi disetiap tahapan penimbunan.

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)0.004
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 score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.005

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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