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Record W1599418143 · doi:10.18926/fest/11431

Laboratory Verification of a Proposed New Method to Determine the In-Situ Effective Porosity of Unsaturated Soil

2006· article· en· W1599418143 on OpenAlexaff
Makoto Nishigaki, Mitsuru Komatsu, John Apambilla Akudago, Yoshihide Shinshi, Kenji Kawakami, Koji Kumamaru

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2006
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsPorositySaturation (graph theory)WettingReflectometrySoil waterDegree of saturationWater contentSoil scienceGeotechnical engineeringMaterials scienceMineralogyGeologyComposite materialTime domainMathematics

Abstract

fetched live from OpenAlex

A laboratory validation of a proposed new method of determining the in-situ effective porosity of unsaturated soils was carried out on unsaturated river sand. The proposed method consists of boring a small diameter hole into the soil and inserting an Amplitude Domain Reflectometry (ADR) probe at the bottom part of the hole. Water is supplied into the hole till saturation and later de-saturated. The water content with time is determined from the ADR probe voltage potential readings. The effective porosity is determined from the difference between the saturated and de-saturated water contents. However, in the laboratory, the water is supplied through the top and bottom parts of the sample. From the experiments the obtained effective porosity ranged from 0.28 to 0.29 for wetting from the top with an average of 0.287 and 0.29 to 0.30 for wetting from the bottom with an average of 0.293. The determined effective porosity ranged from 71.7-73.3% of the real initial porosity of 0.40 of the river sand. The maximum attained degree of saturation during the experiment was 91%. The results indicated that the method will be suitable and useful in determining the effective porosity of medium grained unsaturated soils.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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 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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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