Validation And Potential Improvement of Soil Survey Maps Using Proximal Soil Sensing
Bibliographic record
Abstract
ABSTRACT There is potential use of proximal soil sensors (PSS) to contribute to soil surveys and improve their results, and this study focused on the evaluation of this potential. An analysis using a high-resolution soil survey (1:5,000), terrain data, and an ensemble of PSS (gamma ray emission, ground penetrating radar – GPR, apparent electrical conductivity from electromagnetic induction, and galvanic contact) was conducted. First, a geostatistical analysis was performed to characterize the spatial variability of each variable for each sensor and interpolate the data to a common support. The GPR data presented well-delineated groups of depths with similar spatial structure. These groups matched the field soil horizon depths, thus representing the potential for this sensor in soil characterization. A significant correlation was found between most of the variables from each sensor. However, no complete agreement was observed among the data from different PSS. In addition, a visual comparison of the maps showed that each PSS captured the soil spatial variability of the field and delineated regions distinctively. To validate the soil separation provided by the high-resolution soil survey and evaluate the capability of the PSS to distinguish the different soils, an analysis of variance was performed. Although none of the sensors could differentiate all the soils in the field, maps containing an overlay between sensors and soil models provided an important insight: overall, the soils were located correctly but the boundaries needed to be adjusted. Spatial clustering was used to perform a multivariate analysis of the data. A final map containing well-delimited homogenous PSS-based zones was obtained. Accordingly, it is possible to conclude that this approach and the resulting maps can be used to improve the delineation of boundaries between different soil types.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".