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Record W2025105905 · doi:10.1029/2008wr007616

Time‐lapse electrical resistivity monitoring of salt‐affected soil and groundwater

2009· article· en· W2025105905 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater Resources Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsGeoscience BCUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Association of Petroleum Producers
KeywordsElectrical resistivity tomographyElectrical resistivity and conductivityGroundwaterSoil sciencePlumeHydrology (agriculture)Soil waterGroundwater rechargeGeologyTRACEREnvironmental scienceMineralogyAquiferGeotechnical engineeringMeteorology

Abstract

fetched live from OpenAlex

In order to develop and test a methodology for incorporating time‐lapse electrical resistivity imaging (ERI) into the monitoring of salt‐affected soil and groundwater, a multifaceted study including time‐lapse electrical resistivity imaging, push tool conductivity (PTC), and core analysis was conducted to monitor the movement of a saline contaminant plume over the span of 3 years. The survey was done on a field site containing salt‐affected soils and groundwater to depths of over 7 m. The site contained a tile drain system at approximately 2 m below ground level. Temperature and saturation changes were accounted for in electrical conductivity (EC) measurements to isolate changes in electrical conductivity due to changes in salt distribution. ERI inversion parameters were selected so that the inverse models gave the best match to PTC depth profiles and the best correlation with core EC data. A strong correlation between the core data and the ERI results was observed. Time‐lapse ERI difference images showed that the subsurface EC distribution was consistent with preferential solute removal above the tile drains in some locations. The ERI‐delineated reduction in solute concentration is consistent with nonuniform flushing due to depression‐focused recharge. The addition of time‐lapse ERI to the study allowed delineation of details of solute redistribution that would not have been possible with point measurements alone.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.367

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.298
Teacher spread0.267 · 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