Surface Albedo and Soil Heat Flux Changes Following Drilling Mud Application to a Semiarid, Mixed‐Grass Prairie
Bibliographic record
Abstract
Drilling mud systems are used by the petroleum industry to facilitate and expedite the drilling of oil and natural gas wells. In western Canada, spent water‐based muds (WBM) are often applied to cropland and native prairie at low application rates as a disposal option. We speculate that application of drilling waste on native prairie in semiarid climates will alter the soil surface energy balance and adversely affect soil biophysical processes and subsequently ecosystem productivity. This study was initiated to examine the effect of application rate (0, 40, and 80 m 3 ha −1 ) of summer‐applied WBM on surface albedo (α), soil temperature, and soil heat flux at the 0.05‐m depth ( G 0.05 ) and at the surface ( G ) over 42 d following application. Our results provide evidence of a significant alteration of these micrometeorological parameters with WBM application. Surface albedo decreased by 15% (relative to the control) with the application of 40 m 3 ha −1 mud and by an additional 3% when this mud rate was doubled. The lower α meant that a greater proportion of incoming shortwave solar radiation ( R si ) was available for partitioning into components of the surface energy balance, including G This coincides with observed increases in G 0.05 , G , and soil temperature at the 0.025‐m depth, with higher WBM. By modifying heat flow and temperature in the root zone, WBM application may alter critical ecosystem biophysical and physiological processes, with important implications for overall biological productivity. The direction and magnitude of such changes, in conjunction with all the other ecological effects of WBM, will ultimately define the sustainability of the practice in this fragile 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 | 0.001 |
| 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".