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Record W2055239282 · doi:10.2136/sssaj2000.642469x

Preferential Solute Flow in Intact Soil Columns Measured by SPECT Scanning

2000· article· en· W2055239282 on OpenAlexafffund
Johan Perret, Shiv O. Prasher, Apostolos Kantzas, Kelly Hamilton, Cooper H. Langford

Bibliographic record

VenueSoil Science Society of America Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of CalgaryMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTRACERSingle-photon emission computed tomographySoil waterEmission computed tomographyScannerNuclear medicineTomographyPorous mediumMaterials scienceSpect imagingEnvironmental scienceGeologyPorosityBiomedical engineeringSoil sciencePositron emission tomographyPhysicsOpticsMedicineGeotechnical engineeringNuclear physics

Abstract

fetched live from OpenAlex

Single photon emission computed tomography (SPECT) is an imaging technique that is widely used in medical diagnosis. This technique has never been applied to soils. The objective of this study was to investigate the capabilities of SPECT scanning for visualizing preferential flow in soil. This paper describes the principle of SPECT scanning and its application to tracer breakthroughs in four large undisturbed soil columns (800‐mm length × 77‐mm diam.). This new approach allows real‐time analysis of flow patterns of radioactive tracers in 2‐D using planar imaging, and in 3‐D using the tomographic capabilities of the SPECT scanner. Not only does SPECT scanning provide qualitative data, but it also allows for the quantification of a tracer's spatial distribution. Our results characterized preferential flow very clearly in soil columns. SPECT scanning opens up new avenues for 2‐D and 3‐D tracer studies in porous media such as soils.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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 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

Citations57
Published2000
Admission routes2
Has abstractyes

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