Calibration of the Simple Rainfall Simulator for Investigating Soil Erodibility in Unpaved Roads
Bibliographic record
Abstract
Rainfall simulators have been used for erosion research for more than 50 years now.These are widely used in agricultural soils to assess the infiltration capacity and porosity of soils, and hence learn lessons on the potential of plant roots to penetrate those soils.Recently, rainfall simulators have been very useful to investigate soils detachment by both the raindrops kinetic energy and the subsequent flow shear stress.This has led to notable advances in the understanding of the failure of infrastructures such as unpaved roads due to surface soil loss and formation of erosion features, buried pipes and facilities due to removal of fill materials, as well as bridge scour and embankments failures to mention a few.To help conduct a thorough and rigorous research, rainfall simulators must produce raindrops of the same size as those produced by the natural rainfall.Calibrating rainfall simulators satisfying this key demand of raindrops sizes in the range of 1 mm to 6 mm posed challenges for years, and therefore led to inconsistencies in results from different studies.In this paper, an economical rainfall simulator which can be used for assessing erodibility of soils in unpaved roads was developed.The flour method technique was used to determine the sizes of the raindrops.The mean raindrops sizes were found to be 3.0 mm, 3.2 mm, and 3.5 mm, respectively for the rainfall intensities of 30 mm/hr, 51 mm/hr and 68 mm/hr falling through 2.0 m.In the same order of rainfall intensities, raindrops hit the surface of the tested surfaces by 193.5 µJ, 244 µJ and 301 µJ kinetic energies, which were sufficient to initiate detachment in soils of D50 ranging from about 0.4 mm to 1.5 mm compacted to maximum dry density.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".