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Record W4415564775 · doi:10.3390/land14112125

Assessing Electrical Conductivity and Sodium Adsorption Ratio as Soil Salinity Indicators in Reclaimed Well Sites

2025· article· en· W4415564775 on OpenAlexafffund
Laura Bony, Amalesh Dhar, S. R. Wilkinson, M. Anne Naeth

Bibliographic record

VenueLand · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsSodium adsorption ratioSalinitySulfateSodiumSoil salinitySoil waterChlorideLand reclamationSoil test

Abstract

fetched live from OpenAlex

Electrical conductivity (EC) and sodium adsorption ratio (SAR) are the two most widely used indicators of soil salinity worldwide. However, concerns regarding the use of EC and SAR for assessing soil salinity have been raised by industry, scientists, and regulators. This study examines 22 well sites across two ecoregions, sampling soils from 0 to 1.5 m depths, and hypothesized that EC and SAR may be insufficient indicators of soil salinity during reclamation. Both ecoregions had distinct soil salinity profiles, with greater variability in the upper 0.3 m. Across ecoregions, EC was 1.0–8.4 dSm−1 and SAR was 0.7–9.1. In the dry mixed-grass ecoregion, EC was moderately correlated with SAR from 0 to 0.45 m depths and significantly correlated with all ions above 0.6 m. EC explained 44–56% of chloride variation and up to 51% of sulfate in topsoil. In central parkland, EC correlated with chloride and magnesium at all depths and with calcium at most depths. SAR was strongly correlated with sodium at all depths in both ecoregions, explaining 6–82% of variation, and poorly predicted chloride and sulfate. SAR and EC did not always represent potentially toxic sodium, chloride, and sulfate ions; thus, these ions could be included as indicators, and current reclamation criteria should be modified or interpreted differently based on ecoregions and soil depths.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.240

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 head0.014
GPT teacher head0.275
Teacher spread0.260 · 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 designObservational
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

Citations4
Published2025
Admission routes2
Has abstractyes

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