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Evaluation of Soil/Material Interface Friction and Adhesion of Akure Sandy Clay Loam Soils in Southwestern Nigeria

2012· article· en· W1596705614 on OpenAlexvenueno aff
S. I. Manuwa

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2012
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsLoamSoil waterGeotechnical engineeringGalvanizationWater contentNatural rubberFriction angleMaterials scienceEnvironmental scienceSoil scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

There is the need to develop appropriate and efficient soil engaging tools and implements to optimize energy required to cultivate the land and enhance agricultural productivity and sustainability in Nigeria. Necessary design data which were hitherto scarce for Nigerian soils are therefore required to accomplish the task effectively. Laboratory investigations were carried out to evaluate angle of soil/material friction and coefficient of soil/ material friction necessary in the design of soil-engaging tools and implements. Facilities used in the investigation include soil-material friction device or sliding shear apparatus. Three types of soil investigated were sandy clay loam soils. The structural materials’ surfaces investigated were rubber (RUB), steel (SST), galvanized steel (GAS) and Teflon (TEF). Results show that the coefficient of soil/material friction increased with moisture content to a limit and thereafter decreased. For the materials tested the range was 0.13 - 0.85 in the three soil textures and can be described by polynomial equations for the purpose of prediction. Rubber had the highest coeffi cient of soil/ interface friction followed by smooth steel, galvanized steel, while Tefl on had the least in that order. Key words : Soil; Coeffi cient of friction; Materials; Soil/ tool interface; Adhesion; Models

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.301
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations10
Published2012
Admission routes1
Has abstractyes

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