Soil quality and productivity responses to simulated erosion and restorative amendments
Bibliographic record
Abstract
There is little quantitative information on the interrelatedness of soil erosion, soil quality and soil productivity. A simulated erosion approach was used to quantify erosion and amendment effects on soil quality and subsequent productivity at four southern Alberta sites. Zero, 5, 10, 15 and 20 cm of topsoil were removed (cuts) at each site and subplots were amended with N + P fertilizer, 5 cm topsoil, 70 Mg ha −1 cattle manure or left unamended. Wheat (Triticum aestivum L.) yields in the 2-yr study on the non-amended check plots showed significant correlations with organic C at three of the four sites, and extractable P and inorganic C at all four sites. While manure was the best amendment for enhancing soil productivity, the magnitude of its effect depended on the organic C content of the recipient soil. At an organic C content of 8 g kg −1 on the Lethbridge Dryland site, manure addition increased crop yield by 1.75 Mg ha −1 , compared with only 0.27 Mg ha −1 at an organic C content of 15 g kg −1 . Our results affirm the benefits of soil management practices that reduce erosion risk, preserve soil quality and sustain productivity. Key words: Soil quality, soil productivity, erosion, manure, topsoil, wheat
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".