Spatial prediction and uncertainty estimation of crucial GlobalSoilMap properties - A contextual study in the semi-arid area of western Iran
Bibliographic record
Abstract
Information on the spatial distribution of organic carbon (OC), salinity (EC), and soil pH in the semi-arid region of the Chahardowli Plain in western Iran is limited. Though soil carbon is the most common property mapped in GlobalSoilMap, EC, and pH affect OC content and other soil properties and functions. To study variations in these properties and map their distribution, soil samples were collected at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm. A total of 145 soil samples were collected from 30 profiles. The relationships between soil characteristics and environmental covariates were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. We used a k-fold cross-validation to assess the quality of the predictions. The RF model demonstrated the highest prediction accuracy for all three soil properties. The OC validation results for the RF model show that the R 2 value was between 0.80 and 0.98, the R 2 value for EC was between 0.74 and 0.98, and the R 2 value for pH was between 0.80 and 0.93. The channel network base level (CNBL) was found to be the most crucial covariate in predicting EC, while CNBL and vegetation indices were the most significant covariates in predicting OC. The covariates found to be crucial in predicting pH were the difference in vegetation index (DVI) and slope (S). The bootstrap method was used to compute the prediction uncertainty. The bootstrap method provided reliable estimates of uncertainties associated with these predictions. For all layers and all points, the coverage percentage for OC, EC, and pH was between 80 and 95% at the 95% confidence level. This shows the reliability of the estimated confidence limits. This study indicated that high pH and EC levels are associated with a reduction in soil OC percentage. To provide an accurate representation of OC distribution in any region, it is necessary to report not only an OC map but also maps of EC and pH, as the spatial interrelationship between soil properties highlights the need for estimating pH and EC for better understanding OC variability and soil functioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".