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Record W1964920345 · doi:10.1029/2004gl021112

The effect of vertical measurement resolution on the correlation structure of a ground penetrating radar reflection image

2004· article· en· W1964920345 on OpenAlexaff
Rosemary Knight, Paulette Tercier, James Irving

Bibliographic record

VenueGeophysical Research Letters · 2004
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRadarGeologyRemote sensingGround-penetrating radarReflection (computer programming)Radar imagingResolution (logic)Range (aeronautics)Horizontal and verticalImage resolutionRadar horizonGeodesyOpticsContinuous-wave radarPhysicsMaterials scienceComputer science

Abstract

fetched live from OpenAlex

Geostatistical analysis of a ground penetrating radar reflection image can be used to quantify the maximum correlation direction and the range of horizontal and sub‐horizontal radar reflections. A review of previous work, and an analysis of a photograph of layered sediments, suggest that the vertical resolution of a radar image strongly affects its lateral correlation structure. Numerical modeling was used to generate synthetic radar sections and investigate the effect of the vertical resolution of the radar measurement on the link between the correlation structure of the radar reflections and the true correlation structure of subsurface water content. The horizontal range of the radar reflections decreased as the vertical resolution improved, closely matching that of the water content when the vertical resolution was approximately equal to the vertical range of the water content.

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)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.323
Teacher spread0.287 · 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 designSimulation or modeling
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

Citations11
Published2004
Admission routes1
Has abstractyes

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