Evaluation and modeling of factors influencing the depth of mixing layer in which soil solute releasing from soil to surface runoff
Bibliographic record
Abstract
The depth of mixing layer is one of the important parameters which cannot be assigned a constant value affected by many factors in the slope runoff. The objective of this study was to investigate the effect of slope length and underground biomass on slope runoff, solute transport processes, as well as mixing layer depth. In this study, the experimental plots with the four slope lengths (5, 10, 15, and 20 m) and a width of 2 m were built on the slope with the gradient of 20°. In addition, the plots with the millet or wheat planting were built on the slope. The change of runoff and solute transport was analyzed through simulated rainfall experiments and then to estimate mixing layer depth. The results showed that the runoff rate decreased and more runoff seeped into the slope soil with increasing slope length. Increasing underground biomass also promoted greater rainfall infiltration into the soil. The increase in slope length increased the concentration of solute in runoff, but more underground biomass reduced the nutrients transported with runoff. The effective mixing depth increased with an increase in slope length, but effective mixing depth decreased with increased underground biomass. The modified expression of the equivalent mixing model under different slope lengths and underground biomass could accurately describe the solute transfer process in runoff when compared with complete mixing model and incomplete mixing model based on exponential functions. This research provided a reference for improving the application of mixing layer models in the slope management.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".