Surface Residue Effects on Erosion of Thawing Soils
Bibliographic record
Abstract
Soils that experience freezing and thawing are most susceptible to erosion during the late winter and early spring. Greater than 50% of the total annual erosion may occur during this period in parts of the USA and Canada. In this period the upper layer of the soil profile thaws due to rising temperatures, while the subsurface layer stays frozen, greatly limiting water movement through the soil profile, weakening the surface soil. This experiment was conducted to evaluate the effects of four treatments—residue cover (0, 10, 30, and 80%), soil inclination (5, 9, and 13%), soil type (loess and glacial till), and a frozen vs. non‐frozen subsurface layer—on two response parameters (soil eroded and soil splash) from small laboratory plots. An erosion box with a surface area of 0.13 m 2 received 0.0343 m of simulated rainfall in a 30‐min period. Significantly higher erosion (0.212 vs. 0.152 kg) and soil splash (0.090 vs. 0.066 kg) was observed for the frozen than for the unfrozen subsurface soil layer treatments, respectively. The most erodible condition (13% inclination with frozen subsurface layer) was the most responsive to surface residue cover, 0.335 vs. 0.111 kg eroded soil for 0 vs. 80% residue cover, respectively. The least erodible condition (5% inclination without a frozen subsurface layer) was the least responsive to residue cover (0.161 vs. 0.076 kg eroded soil for 0 vs. 80% residue cover). Residue cover seems very important for reducing soil loss during the soil thawing period, particularly on steep slopes, and may be more important for subsurface frozen conditions than when subsurface frozen layers do not exist.
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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".