Soil Disturbance and the Potential for Erosion After Mechanical Site Preparation
Bibliographic record
Abstract
Abstract Physical soil properties created by three mechanical site preparation treatments (ripper ploughing, disc trenching, and blading) and a control were evaluated to determine the success of these different mechanical site preparation treatments in creating plantable microsites and to estimate the potential for soil erosion created by each treatment. Three sites with fine textured soils and high water contents near Whitecourt, Alberta, Canada), were selected for study. The topography at all sites was similar and characterized by slopes 3.7 to 20% in steepness and approximately 190 to 270 m in length extending from the height of land to stream bottoms. The number of planting sites and the soil characteristics suggest ripper ploughing as the best site preparation treatment in this study, with the hinge microsite as the preferred planting spot. All three treatments significantly improved the physical conditions of the soil compared to the control, although the differences among treatments were small. Soil erosion was observed on areas where blading and ripper ploughing exposed mineral soil. Gullies, which exposed the roots of seedlings, were created by water erosion in the blading treatment area. Sediment deposition in trenches was observed on ripper ploughed areas, and at times, seedlings within this treatment area were partially buried as a result of this soil movement.
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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) | 0.000 | 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".