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Record W2606336752 · doi:10.11159/icgre17.104

Characterisation and Index Properties Correlations for Marlstone and Marly Limestone of Saudi Arabia

2017· article· en· W2606336752 on OpenAlexvenueno aff
Yassir Mubarak Hussein Mustafa, Hamzah M. B. Al-Hashemi, Ahmed H. Bukhary

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersKing Fahd University of Petroleum and Minerals
KeywordsMarlIndex (typography)GeologyMining engineeringGeochemistryComputer sciencePaleontologyWorld Wide Web

Abstract

fetched live from OpenAlex

It has been a common practice to estimate the Unconfined Compressive strength (UCS) of rock through correlations that relate it to other index parameters such as the Brazilian Tensile Strength (BTS) and the Ultrasonic Pulse Velocity 𝑉 𝑝 ; nonetheless, selecting the most appropriate equation to use has always been a challenge considering the heterogeneity of rocks and their variant behaviour.Therefore, the use of equations which are especially developed for certain rock type at a specific location is recommended.This paper suggests 3 equations to predict UCS from BTS, bulk density (𝜌) and 𝑉 𝑝 for marlstone of the Eastern Province of Saudi Arabia.35 core samples are collected and tested in the laboratory for UCS, BTS and 𝑉 𝑝 .Statistical analysis is performed on the experimental results, and subsequently 3 statistical models that relate BTS, 𝑉 𝑝 , and rock bulk density 𝜌 to UCS are developed.Corrected correlation coefficients of the three models are found to be 0.575, 0.663, and 0.641, respectively.Additional reliable equation that relate the bulk density to 𝑉 𝑝 is produced with a corrected correlation coefficient of 0.95.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.179
Teacher spread0.171 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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