Estimating soil erosion intensity in susceptible areas based on a comprehensive stratified sampling system associated with the geographical information system
Bibliographic record
Abstract
An error-free and fast approach for the identification of the area dynamics of soil erosion intensity (SEI) is essential for local governments. In this study, a simple method combination of the vegetation cover, gradient slope, and land use coupling index (vegetation and slope coupling index) model with field sampling unit schemes was explored to estimate the area variations of SEI between the years 1998 and 2009. The obtained results show that the minimum average prediction accuracy (PA) for sampling units was 94% among different land use types, whereas the maximum PA was 98.5%. Although PAs for different land use types showed discrepancies, the trend clearly depicted higher rates of sampling units and lower relative errors. The average fitting accuracies (FAs) of the sampling units were 98.84%, 97.92%, and 97.26%, respectively, based on different proposed strategies, whereas the FAs of the extrapolation results were 94.77%, 87.57%, and 92.41%, respectively. In addition, the extrapolation results were found to be less efficient than the sampling units. However, this is acceptable, considering the field observation workloads and time consumption. Therefore, this study provides a promising scheme for the rapid estimation of the area dynamics of SEI, which will be useful for estimating the SEI in other areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".