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Record W2005850936 · doi:10.1139/t10-034

Steel-shot method for measuring the density of soils

2010· article· en· W2005850936 on OpenAlexvenueno aff
Reed B. Freeman, Chad A. Gartrell, Lillian D. Wakeley, Ernest S. Berney, Julie R. Kelley

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWater contentVolume (thermodynamics)Environmental scienceSoil waterSoil testMoistureSoil scienceGeotechnical engineeringShot (pellet)Process engineeringEngineeringMaterials scienceComposite materialMetallurgy

Abstract

fetched live from OpenAlex

The density of soil is crucial in engineering, construction, and research. Standard methods to determine density use procedures, equipment or expendable materials that limit their effectiveness in challenging field conditions. Some methods require burdensome logistics or have time requirements that limit their use or the number of tests that can be executed. A test method, similar to the sand-cone method, was developed that uses steel shot as the material to which a volume of soil is compared to calculate soil density. Steel shot is easily recovered and reused, eliminating the need for specialty sand and calibrated cones or containers, and allows rapid determination of the volume of displaced soil. Excavated soil also provides measurements of total mass and moisture content. Volume, mass, and moisture content are applied in simple calculations to determine wet and dry densities and unit weight of the soil. Proficiency in performing the test can be achieved with minimal training, and the required kit can be assembled for a reasonable cost. Field uses of the method in dry environments in a variety of soil types demonstrated that the method can produce repeatable results within 2% of the values of soil density determined by traditional methods, with advantages in logistics.

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 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 score0.858
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.240
Teacher spread0.221 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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