Reclamation of Abandoned Natural Gas Wellsites with Organic Amendments: Effects on Soil Carbon, Nitrogen, and Phosphorus
Bibliographic record
Abstract
Organic amendments have been used to restore productivity to disturbed soils such as those on abandoned oil and natural gas wellsites. A study was conducted on three abandoned wellsites in southern Alberta, Canada to examine the effects of one‐time applications of alfalfa ( Medicago sativa L.) hay or beef cattle ( Bos taurus ) feedlot manure compost on soil properties under continuous wheat ( Triticum aestivum L.). The base amendment rate (1×) [dry wt.] was 5.3 Mg ha −1 for compost and 3.1 Mg ha −1 for alfalfa. The five amendment rates of 0, 1×, 2×, 4×, and 8× were soil‐incorporated at the wellsites. Although approximately twice as much C was applied with alfalfa than with compost, final SOC content was similar for the two amendment treatments, indicating the greater stability of compost‐derived C. Nitrate N content in the 0‐ to 60‐cm depth was not affected by compost rate (mean 213 kg ha −1 ) but increased by 7.78 kg ha −1 for each Mg ha −1 increase in alfalfa rate. This result reflects the greater stability of compost‐N compared with alfalfa‐N and suggests a lower risk of NO 3 –N leaching with compost application. Compost rates >20 Mg ha −1 resulted in excessive extractable P build‐up in the topsoil (up to 95.7 mg kg −1 ), which may pose environmental risk to surface water. We recommend amending wellsites with up to 12 Mg ha −1 of alfalfa or <20 Mg ha −1 of compost during reclamation to improve C storage and nutrient cycling while minimizing nutrient loss to water systems.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".