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Record W1996586691 · doi:10.1109/icgpr.2010.5550073

Inference of vertical soil moisture distribution using high-frequency CMP and reflection traveltime analysis

2010· article· en· W1996586691 on OpenAlexafffund
Colby M. Steelman, Anthony L. Endres

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGround-penetrating radarWater contentGeologySoil scienceReflection (computer programming)PetrophysicsSoil horizonRadarPorositySoil waterGeotechnical engineering

Abstract

fetched live from OpenAlex

High-frequency ground-penetrating radar (GPR) surveys were used to investigate temporal water content variations in a vertical soil column characterized by stratified clean sand deposits over multiple annual cycles. Reflection profiling and common-midpoint (CMP) soundings were coincidently performed using 900 MHz antennas across a 2 m intensive monitoring profile. Our ability to identify fixed reflection events along a vertical soil profile permits inference of soil water flux across defined soil intervals in a non-invasive manner. Soil moisture contents were estimated from two-way traveltime measurements between seasonally coherent stratigraphic interfaces in the upper 2-3 m of soil. Interval thicknesses between stratigraphic interfaces were estimated from normal-moveout velocity analysis of coincidently collected CMP soundings. Interval traveltimes from reflection profiles were then converted to wave velocity using the interval thickness estimates and a volumetric water content estimate using an appropriate petrophysical relationship. The GPR effectively characterized long (e.g., seasonal trends) and short-period (e.g., distinct wetting events) variations in vertical soil moisture distribution.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.226

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 head0.013
GPT teacher head0.284
Teacher spread0.271 · 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.

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

Citations0
Published2010
Admission routes2
Has abstractyes

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