Effect of subsoiling and injection of pelletized organic matter on soil quality and productivity
Bibliographic record
Abstract
Leskiw, L. A., Welsh, C. M. and Zeleke, T. B. 2012. Effect of subsoiling and injection of pelletized organic matter on soil quality and productivity. Can. J. Soil Sci. 92: 269-276. Subsoil compaction is a widespread problem in most reclamation and other industrial operations. The objective of our research was to evaluate effectiveness of coupling deep subsoiling with injection of 20 Mg ha-1 organic matter pellets. Research was conducted at seven sites on a pipeline right-of-way in central Alberta. Treatments were subsoiling, subsoiling with pellets and a compacted right-of-way (control), established in spring and fall 2009. Treatment effects on soil physical properties and nutrient status were assessed in fall 2009 for spring-established sites and on all sites in fall 2010. Density and height of canola plants were determined in late summer 2010. Relative to the control, subsoiling with pellet treatments had lower bulk density in the 20- to 40-cm depth interval (up to 40%) in 2010, particularly in clay-loam soils. This treatment often had higher available phosphorus and total organic carbon in 2010, and total nitrogen in spring treated sites in 2009. Relative to the control, subsoiling with pellets had 46% higher canola plant density in clay loam soils of fall-treated sites. Subsoiling with pellets is recommended on heavy-textured, compacted soils to alleviate compaction and increase plant productivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".