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Record W2027960590 · doi:10.1109/tmtt.2012.2198235

Buried Object Characterization Using Ultra-Wideband Ground Penetrating Radar

2012· article· en· W2027960590 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2012
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGround-penetrating radarRadarRemote sensingRadar imagingPermittivityComputer scienceAcousticsGeologyElectronic engineeringEngineeringTelecommunicationsDielectricPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, a method to characterize buried nonmagnetic objects in ground using ultra-wideband (UWB) ground penetrating radar (GPR) is proposed. In this method, UWB pulses are radiated by the radar, while scattered signals from the ground with the buried object are received. The received signals are then post-processed to estimate the depth, thickness, and electrical properties of the buried object. A constant depth and thickness is enforced at all frequencies while the signals are processed to extract the buried object characteristics, resulting in more accurate estimations and reduced processing time. In addition, path loss due to the close proximity of the radar to the ground is compensated analytically. The applicability of the proposed method is validated with several planar objects and a boulder that we typically encounter in the construction industry. The proposed method can achieve sufficient reliability in estimating the permittivity of buried objects for the purpose of material identification. Incorporating the proposed method into the GPRs enhances their existing imaging ability by adding material identification capability.

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.

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: none
Teacher disagreement score0.569
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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