Soil structure assessment using pedomorphological descriptors – SSAPD in Quebec
Bibliographic record
Abstract
Soil structure is a key factor influencing numerous processes in soil which is not commonly measured but can be assessed in the field using different manual/visual methods. The aim of this work was to develop a visual method based on standardized pedomorphological descriptors which offer a better framework than soil attributes used in the popular VESS technique and to validate its use under Quebec agricultural conditions. The Soil Structure Assessment using Pedomorphological Descriptors (SSAPD) method was developed using data collected from a large-scale soil health inventory and a panel of soil experts to assign rates and weights to pedomorphological descriptors and to calculate a score. The scores produced by SSAPD showed a good relationship with VESS. SSAPD was also able to discriminate between cultivated and control soils with better soil physical condition. Pearson’s correlations were generally weak across all textural groups and horizons; however, a few moderate correlations were observed with expected trends in the Ap horizon. Generalized additive mixed model (GAMM) were used to analyze the relationship between SSAPD and soil properties. The fitted models revealed non-linear relationships between SSAPD and most of the analyzed soil properties across different textural groups specially for clayey and loamy soils. SSAPD’s scoring methodology, based on pedological descriptors and a system of ratings and weights, offers broad potential for calibration and fine-tuning across various parent materials, soil textures, and depths.
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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 | 0.001 | 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) | 0.001 | 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 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".