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Record W4414826380 · doi:10.33084/mits.v13i3.10869

Analisis Kemampuan Pasir Dari Pantai Nirwana Kota Padang Dan Pasir Dari Pantai Kawasan Mandeh Pesisir Selatan Sebagai Pengganti Pasir Ottawa Dalam Pengujian Sandcone

2025· article· en· W4414826380 on OpenAlexaboutno aff
Sicilia Afriyani Cici, Afrizal Putra Prices, Angga Putra Arlis, Wahyu Araska, Vero Gusri Vernando, Alya Miftahul Husni, Effendi Effendi, Winda Fitria

Bibliographic record

VenueMedia Ilmiah Teknik Sipil · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
Fundersnot available
KeywordsGradationSpecific gravityGranulometryParticle-size distributionHydrology (agriculture)

Abstract

fetched live from OpenAlex

Ottawa sand has long been used as a standard material in geotechnical laboratory tests, particularly in grain size distribution and Sand Cone tests, due to its uniform particle size, purity, and stable physical properties. This study explores the potential of using beach sand from Nirwana and Mandeh as substitutes for Ottawa sand. The research process began with problem identification, followed by a literature review, sample collection, laboratory testing, and data analysis. The results show that the specific gravity of Nirwana sand (2.77) and Mandeh sand (2.72) are relatively close to Ottawa sand (±2.65). In terms of the coefficient of uniformity (Cu), Nirwana sand (2.34) is closer to Ottawa sand (2.17) compared to Mandeh sand (2.94). Regarding the coefficient of curvature (Cc), Nirwana sand (0.72) performs better than Mandeh sand (0.66), although both remain below the ideal range (1–3). Overall, Nirwana sand demonstrates greater potential as an alternative to Ottawa sand, with improvements in gradation recommended to achieve optimal performance.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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 teacher head, not a consensus.

Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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