Soil chemical properties following a one‐time spent drilling mud application on native prairie
Bibliographic record
Abstract
Abstract Landspraying while drilling (LWD) is an approved disposal method for water‐based drilling mud (WBM) systems in western Canada, where the mud is applied either on arable or vegetated land. This study examined the effects of a single LWD application (0, 15, 20, 40, or 80 m3 ha−1) on native prairie soil properties. Results from the study showed a significant increase in Na concentration and sodium adsorption ratio (SAR) in the 0‐ to 7.5‐cm depth. However, the highest SAR attained (3.46) after application at the 80 m3 ha−1 rate remained below levels considered detrimental to soil structure. Electrical conductivity (EC) also increased with the LWD rate but peaked at levels (447 μS cm−1 in the 0‐ to 2.5‐cm depth) much lower than the threshold for most plant species, and the effects on EC had disappeared by the end of the first year of mud application. Available P concentration (modified Kelowna method), averaged across sampling times and the two depths (0‐ to 2.5‐ and 2.5‐ to 7.5‐cm), increased from 7.4 to 11 mg kg−1 as LWD rate increased from 0 to 80 m3 ha−1. Although the available P remained at concentrations suboptimum for most crops, such concentrations may impact native prairie vegetation adapted to very low P. Drilling mud applications generally had no significant effect on available N. The application of WBM systems on the native prairie at recommended rates in western Canada may not be detrimental to soil quality and plant growth in this ecosystem.
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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) | 0.000 | 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 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".