Rebuilding the Fertility and Productivity of Eroded Knoll Soils
Bibliographic record
Abstract
Historical erosion (water, wind, and tillage) of upper-slope knolls within hummocky fields have typically resulted in the translocation of native fertile topsoil to lower-slope positions, leaving the soils remaining at these higher landscape positions with low organic matter, poor fertility, along with reduced water infiltration and holding capacity. A three-year rotational field study was established to evaluate the productivity of spring wheat, field pea, and canola growing on two eroded knoll locations with and without nine different soil fertility treatments: side-banded mono-ammonium phosphate; side-banded zinc sulfate; side-banded copper sulfate; side-banded ZnSO4 + CuSO4; side banded MAP + ZnSO4 + CuSO4; composted solid cattle manure (SCM) broadcast and incorporated; broadcast and incorporated SCM followed by side-banded ZnSO4 + CuSO4; side-banded Zn-containing char; and historically eroded topsoil mechanically transplanted back onto the knoll from an adjacent depressional area. Based on the first growing season results, it appears that even under record-breaking dry growing season conditions, restoring eroded topsoil back to the eroded knoll landscape position is the most effective method of increasing spring wheat crop productivity. Positive responses of wheat and pea to MAP and Zn respectively, as well as trend towards benefit from SCM, indicate potential benefits from these amendments as well, albeit smaller than replacing the original topsoil lost by erosion.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".