Design of a rainfall simulator to measure erosion of reclaimed surfaces
Bibliographic record
Abstract
Rainfall simulation is a useful tool in the analysis of soil erosion. The use of rainfall simulators has become more widespread with the development of automated instrumentation and control systems which offer a physically based system of predicting soil erosion. A variety of simulator designs have been used. This paper describes a rainfall simulator designed for analysis of erosion on steep (2.5H: 1V) reclaimed sand slopes at two oil sand mines near Fort McMurray, Alberta. The rainfall simulator applies artificial rain on a 225 m² test site divided into two side-by-side test plots. Most rainfall simulators have used a constant intensity of rainfall throughout a given simulation event. The rainfall simulator designed for this project can vary the rainfall application rate in fifteen discrete rainfall intensity increments. Therefore, it is capable of simulating a variety of non-uniform rainfall hyetographs. The rainfall simulator consists of a system of seven nozzles on each of 27 vertical support pipes. A combination of nozzles was used to simulate a desired uniform or non-uniform rainfall hyetograph with intensities ranging from 10 mm/h to 200 mm/h. The rainfall simulator also successfully simulated extreme historic and synthetic hyetographs.
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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 | 0.001 |
| 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 | 0.001 | 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".