Accuracy Assessment of Sequential Indicator Simulation in Three-dimensional Prediction of Soil Texture
Why this work is in the frame
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Bibliographic record
Abstract
Equiprobable realizations of soil textural maps can be drawn using Sequential Indicator Simulation (SIS), which reflects the probability of occurrence of each texture and is constrained by the observed textures at the observation sites. However, the SIS is not an error-free technique, and the accuracy of these maps should be checked before they are used as basic information for precision agricultural- and environmental-related studies. This article assesses the accuracy of using SIS in the three-dimensional prediction of soil texture. A soil data set (139 profiles) with five types of textures distributed in a 15-km2 region was first collected and then randomly sub-divided into a training set (85 profiles) and a validation set (54 profiles). Second, 100 realizations were obtained by SIS using the training set. Finally, the prediction capacity was assessed using independent validation set and probability of correct prediction as criterion. Results show that 43.59% of total observations can be correctly predicted while the accuracy varies among textures and depths. The dominant textures in the data set have higher accuracy (>42.49%), while the textures with less proportion (<28.86%) were poorly predicted. The SIS performed better for the near-surface depth (0–0.5 m) than deeper depths (0.5–2.0 m). Therefore, further improvement in simulation of soil texture is necessary as correct predictions of these minor textures and deeper depth textures were very low.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it