Comparison of soil-to-water suspension ratios for determining electrical conductivity of oil-production-water-contaminated soils
Bibliographic record
Abstract
Soil salinity caused by oil-production-water (brine) contamination is a major issue in regions of oil and gas development. However, rapid site assessment tools such as soil-to-water suspension electrical conductivity (EC) methods and conversion equations have not been previously calibrated and validated for brine-contaminated soils. Our objective was to compare three soil EC methods and derive conversion equations for EC values commonly observed at brine-spill sites. Brine-contaminated soils from western North Dakota were assessed for salinity. Electrical conductivity was determined using 1:1 and 1:5 soil-to-water suspensions (EC 1:1 , EC 1:5 ) and saturated paste extracts (EC e ). Soil EC equilibration times for soil-to-water suspensions were also assessed. Significant relationships (r 2 = 0.91 to 0.97, P < 0.0001) existed among all methods for EC values ranging between 0 and 126 dS m −1 . Conversion equations were developed based on these relationships and then validated with an independent data set. These new equations reduced EC e prediction errors by 2 to 4.5 times when compare with 14 predictive equations reported in the literature. The conversion equations developed here are recommended for use in remediation efforts when converting EC 1:1 and EC 1:5 data to EC e on brine-contaminated and noncontaminated soils where EC e is highly correlated to Na concentrations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".