Modeling the spatial distribution of sand, silt, and clay particles based on GlobalSoilMap and Limited Data
Bibliographic record
Abstract
Many regions of Iran lack digital map of soil properties. The Chahardowli plain in western Iran is one of these areas. Due to the importance of sand, silt, and clay components, having quantitative and continuous data on abrupt changes in these two properties in this area is very critical. Therefore, to study sand, silt, and clay, samples were taken at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm, according to GlobalSoilMap. Finally, 145 samples were collected from 30 soil profiles. The significant covariates were selected by Random Forest Recursive Feature Elimination (RF-RFE). Relationships between these characteristics and environmental predictors were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. The accuracy and precision of the models used for all three particles showed that the RF model had the most accurate prediction with R2 and RMSE of 0.82 and 2.34 for clay, 0.80 and 3.87 for sand, and 0.85 and 2.89 for silt, respectively. In this study, terrain-based variables had a greater impact on improving accuracy than remote-sensing variables. The current study showed that even with limited information, digital mapping of sand, silt, and clay particles under GlobalSoilMap and the use of environmental factors can provide acceptable results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".