Soil responses to topsoil replacement depth and organic amendments in wellsite reclamation
Bibliographic record
Abstract
Changes in soil properties reflect the success or failure of reclamation practices on abandoned wellsites. We examined the effect on soil properties of four (0, 50, 100 and 150%) topsoil replacement depths (TRD) and five amendment treatments [compost, manure, wheat (Triticum aestivum L.) straw, alfalfa (Medicago sativa L.) hay, check] aimed at reclaiming three wellsites (Strathmore, Hesketh and Rosedale) in southcentral Alberta. TRD treatment differences were consistent across all wellsites, with 30 to 32% higher soil organic carbon (SOC) on the 150% TRD compared to the 0% TRD. Initially, the alfalfa treatment showed higher levels of nitrate-nitrogen (e.g., 26 mg kg-1 vs. 3 to 7 mg kg-1 for the other amendment treatments in the 15- to 30-cm depth at Strathmore in fall 1998), which was related to its rapid breakdown and mineralization of organic N. After 40 mo (June 1997-October 2000), the average amounts (n = 3 wellsites) of added C conserved near the soil surface were: compost (65 ±10% SE) > manure (45 ±16% SE) > alfalfa (28 ±11% SE) > straw (23 ± 6% SE). Our results show that organic amendments play an important role in improving soil properties related to long-term productivity of reclaimed wellsites, especially where topsoil is scarce or absent. Key words: Wellsite reclamation, topsoil depth, organic amendments, soil quality
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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 | 0.001 |
| 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 | 0.001 | 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".