Wheat yield and soil properties reveal legacy effects of artificial erosion and amendments on a dryland Dark Brown Chernozem
Bibliographic record
Abstract
Erosion leads to substantial loss of soil productivity. To abate such decline, amendments such as manure or fertilizer have been successfully employed. However, the longevities of erosion and soil amendment legacy effects are not well quantified. In 1957, a Dark Brown Chernozem soil at Lethbridge, AB, was land-levelled, creating three degrees of topsoil removal or erosion: noneroded, moderate erosion, or severe erosion. Two amendment studies (1980–1985 and 1987–1991) were superimposed on the erosion treatments. Both studies were cropped to spring wheat (Triticum aestivum L.) from 1993–2010 to examine legacy effects of erosion and soil amendments on wheat yield and soil properties. Without amendment, mean wheat yield under moderate erosion was 40% of the noneroded treatment, whereas severe erosion was 34% of the noneroded treatment, 36–42 yr (1993–1999) after erosion. Under moderate or severe erosion, the restorative power of manure diminished substantially in the first 10–15 yr following cessation of addition, but then levelled off resulting in wheat yields up to 35% higher than equivalent nonamended treatments. Legacy effects of erosion (54 yr) and amendment (27–31 yr) on soil organic carbon and total nitrogen were also observed.
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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".