Effects of oil field brine wastewater on saturated hydraulic conductivity of smectitic loam soils
Bibliographic record
Abstract
Spills of brine wastewater produced during oil well drilling are occurring more frequently in the Great Plains, resulting in crop production loss on affected soil. Remediation requires removal of salt from the topsoil, which might be accomplished by leaching to subsurface horizons or subsurface drains. A laboratory study determined the effects of brine on saturated hydraulic conductivity (Ks) of four nonimpacted surface soils from western North Dakota, USA. Repacked soil cores were subjected to saturated water flow, followed by one pore volume of brine. Subsequent saturated water flow leached brine from the soil and reduced Ks as much as 97% (0.086–0.003 cm h−1) within 24 h. Effluent total dissolved solids (TDS) approached 250 000 mg L−1 then declined (5 mg L−1) with continued leaching, but Ks did not increase. Removal of soluble salts during leaching increased the relative sodium concentrations (ESP > 55), causing clay swelling/dispersion and reduced Ks. Postbrine gypsum application (11.2 Mg ha−1) to replace exchangeable sodium with calcium did not improve Ks. This evidence suggests that if subsurface drainage is used for reclaiming brine-impacted soils that special attention be given to where dispersion/swelling is occurring, leaching water quality, and closely positioning calcium amendments within the high sodium zones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".