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Record W2522895876 · doi:10.1109/icgpr.2016.7572641

A theoretical method to relate the relative permittivity and thermal conductivity of sands

2016· article· en· W2522895876 on OpenAlexaboutno aff
Aaron J. Rubin, Carlton L. Ho

Bibliographic record

Venue2016 16th International Conference on Ground Penetrating Radar (GPR) · 2016
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsRelative permittivityPermittivityThermal conductivityDielectricMaterials scienceSaturation (graph theory)Vacuum permittivityRelative densityConductivityElectrical resistivity and conductivityComposite materialMineralogyGeologyMathematicsChemistryPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

A theoretical method to relate the electrical relative permittivity (dielectric constant) and thermal conductivity of sands is presented. Extensive previous work has been done to relate the physical state (void ratio, water content, and saturation percent) of sandy soils to the bulk thermal conductivity and bulk relative permittivity respectively. These parameters have always been observed isolated from each other. However, both the bulk thermal conductivity and bulk relative permittivity are primarily dependent on the same physical characteristics. Therefore, it should be possible to estimate the thermal conductivity of a soil based on the relative permittivity measured (or vice versa). The objective of this research is to show that this estimation is possible based on actual laboratory measurements. Thermal conductivity and relative permittivity measurements were conducted on prepared bench scale specimens of dry Ottawa Sand at varying density. Thermal conductivity was measured using a thermal needle technique and relative permittivity was measured using a Dynamax TH2O probe. Based on the data collected, there appears to be a linear relationship between the two properties. A correlation is proposed based on the data collected that allows for one to calculate thermal conductivity directly from the relative permittivity measurement. Based on the data collected it appears that the theory that these properties could be estimated from each other is valid for dry sand.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.999

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 integrity0.0000.000
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 head0.035
GPT teacher head0.308
Teacher spread0.272 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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