Crop response to topsoil replacement depth and organic amendment on abandoned natural gas wellsites
Bibliographic record
Abstract
The reclamation success of abandoned wellsites in agricultural areas depends on their capacity to sustain levels of biomass production similar to those which existed prior to soil disturbance. We examined the effect of four (0, 50, 100 and 150%) topsoil replacement depths (TRD) and five amendment treatments [compost, manure, alfalfa (Medicago sativa L.) hay, wheat (Triticum aestivum L.) straw, check) in the reclamation of three wellsites in south-central Alberta. The largest incremental effect of TRD on spring wheat response in the 4 yr (1997-2000) study occurred between the 0 and 50% treatments. Increasing TRD from 100 to 150% had little impact on crop response. Of the 20 treatments (four TRD × five amendments), the reclamation capacity of the 100% TRD-compost treatment ranked highest, being 19% higher than the baseline treatment (100% TRD-check). The lowest-ranking treatment overall , was the 0% TRD-straw treatment, which yielded 64% of the baseline treatment. Crop yield responses to organic amendments were larger when the recipient soil was lower in organic matter. Positive responses to one-time applications of organic amendments in the short-term (4 yr), advocate their use in soil reclamation of abandoned wellsites, despite a lingering question about the longevity of their beneficial effects. Key words: Soil reclamation, topsoil, organic amendments, natural gas wellsite
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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".