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Record W4206091681 · doi:10.1109/lgrs.2021.3140097

Broadband Soil Permittivity Measurements Using a Novel De-Embedding Line–Line Method

2022· article· en· W4206091681 on OpenAlexaff
Hafize Hasar, Uğur Cem Hasar, Yunus Kaya, Taşkın Öztaş, Mustafa Y. Canbolat, Nevzat Aslan, Mehmet Ertuğrul, Omar M. Ramahi

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNotationLine (geometry)PermittivityCalibrationDielectricEmbeddingMathematicsRelative permittivityMaterials scienceMathematical analysisGeometryComputer scienceOptoelectronics

Abstract

fetched live from OpenAlex

A new de-embedding line–line method has been proposed for accurate complex relative permittivity ($\varepsilon _{r}$) determination of soil samples loaded into an EIA 1-5/8” coaxial transmission line measurement system. The method has three main features. First, it bypasses the requirement of calibration of this system by using only two identical coaxial lines with different lengths. Second, it does not need any numerical technique for$\varepsilon _{r}$determination. Third, it does not require knowledge of electromagnetic properties and thickness information of the bead used for supporting soil samples. The method is next validated by simulations performed using a full 3-D electromagnetic simulation program (CST Microwave Studio) and by$\varepsilon _{r}$measurement of a polyethylene (PE) material. Finally,$\varepsilon _{r}$values of three air-dried and water-saturated soil samples having 90% or more sand content with different electrical conductivities (ECs) and gathered from different areas of the city Gaziantep in Turkey, were measured.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.042
GPT teacher head0.293
Teacher spread0.251 · 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 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

Citations12
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Geoscience and Remote Sensing LettersSame topicSoil Moisture and Remote SensingFrench-language works237,207