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Record W4220733235 · doi:10.3390/en15062067

Application of the Horizontal Soil Stratification and Lateral Profiling Methods for 3D Mapping of the Soil Electrical Resistivity

2022· article· en· W4220733235 on OpenAlexaff
Wesley Pacheco Calixto, Carlos L. B. Silva, Viviane M. Gomes, Márcio R. C. Reis, Antonio M. Silva Filho, A. Paulo Coimbra, Gabriel Wainer

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCarleton University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoEmpresa Brasileira de Pesquisa AgropecuáriaFundação de Amparo à Pesquisa do Estado de GoiásCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsDiscretizationElectrical resistivity and conductivityStratification (seeds)Soil scienceSoil waterSoil resistivityInterpolation (computer graphics)Geotechnical engineeringGeologyComputer scienceMathematicsEngineeringArtificial intelligenceMathematical analysisElectrical engineering

Abstract

fetched live from OpenAlex

This work presents a methodology for horizontal soil stratification and a device for 3D soil mapping using the discretization of the Wenner’s method. The methodology starts from the application of the lateral profiling method in conjunction with the Wenner’s method to find the experimental resistivity curve. The Sunde’s algorithm is used to construct the theoretical resistivity curve. By utilizing these experimental and theoretical resistivity curves, it is possible to apply the optimization process to find the parameters: electrical conductivity and thickness of each soil layer. The area under study is discretized and the results related to all subareas are unified using interpolation process. The whole process which results in 3D soil mapping may be automatically produced using the proposed device.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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