Erosion Pins: Installation, Readings, and Calculations of Soil Losses under the Effect of Hydrogel
Bibliographic record
Abstract
Erosion pins are considered a simple and inexpensive way to estimate soil losses due to erosion and have been used in different environments with different degrees and types of erosion. Despite the advantages of this technique, there is a shortage of studies that demonstrate how data systematization and soil loss calculations are performed using this technique. Therefore, this study aimed to present the step-by-step data systematization process of erosion pins obtained in the field and the calculation of soil losses, with the measurement of soil losses under the effect of hydrogel. Readings from 16 pins installed in an area cultivated with soursop trees planted on contour lines and associated with stone rows were monitored weekly between 19/02 and 19/03/2022 by measuring the distance between the soil surface and the end of each pin. The readings were organized by pin and date of measurement. Subsequently, soil lowering and burial, soil density, useful area of each pin, and soil loss in kg/m3 and Mg/ha-1 were determined, enabling statistical analysis and technical interpretation of the data.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".