Comparing direct and indirect approaches to predicting soil texture class
Bibliographic record
Abstract
Soil texture, or the relative proportions of sand, silt, and clay, is a key soil attribute that influences many important physical, chemical, and biological properties of soils. Digital soil mapping is increasingly used to predict soil texture; however, few comparisons have been made between direct prediction of a texture class, and the indirect prediction of texture class by first predicting sand, silt, and clay content, and subsequently converting the predictions to a texture class. We predicted soil texture class for the 5–15 and 30–60 cm depth intervals of the Ottawa soil survey project using direct and indirect approaches which yielded a similar overall accuracy (28–36%) and kappa (0.19–0.27). The predicted soil maps had a similar spatial distribution of soil texture classes. We then used the Euclidean distance between the texture classes to adjust the model performance metrics, revealing the indirect approach provided the better soil texture class prediction. When comparing the predictions, the 5–15 and 30–60 cm maps were in perfect agreement for 53% and 42% of the study area, respectively, and in both cases texture class predictions were within one texture class for over 87% of the map area. For many studies, including legacy soil surveys, texture class information is available, and particle size distribution data are generally lacking. This study confirms that direct prediction of soil texture class performs almost equally with indirect prediction.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".