Quantifying spatial uncertainty of soil organic matter content using conditional sequential simulations: A case study in Emilia Romagna Plain (Northern Italy)
Bibliographic record
Abstract
The development and the application of soil organic matter (SOM) indices have recently received much attention by soil scientists. Inventories of soil carbon for modeling, monitoring and mapping programs are currently being realized at different scales, ranging from global to national, to sub-regional, in order to provide operative tools for land use planners and decision makers. This paper presents the results of a research project on the estimation of SOM spatial distribution in the Emilia Romagna Plain, focusing on the effect of spatial uncertainty on the development and mapping of SOM content. In this work, the analysis has been carried out adopting and comparing two distinct geostatistical approaches to assess the uncertainty regarding the content of SOM jointly over several locations on a regular grid: a parametric approach based on the assumption of a multi-gaussian data distribution and a non-parametric approach based on the transformation of data in indicator variables through selection of operatively significant sill values. Two different SOM content class maps are presented with associated uncertainty. The two maps, although almost coincident in terms of SOM content class distribution in the study area, show a significantly different overall degree of spatial uncertainty, with the non-parametric approach based map showing the highest overall accuracy. Key words: Soil organic matter inventory, spatial variability, stochastic simulations, indicators uncertainty
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".