Soil texture influences on soil health scoring functions in Ontario agricultural soils: a possible framework towards a provincial soil health test
Bibliographic record
Abstract
Since soil health is impacted by inherent soil properties, it is, therefore, challenging to apply the same soil health frameworks across multiple regions and soil types. Here, we examined the effect of soil textural group (coarse, medium, and fine) on four soil health indicators of soils sampled from diverse agricultural systems across Ontario. Scoring functions were developed by calculating cumulative normal distributions, using the mean and standard deviation of each soil health indicator, for three or five soil textural groups. For each soil health indicator, soil health scoring values were provided using the “more is better” approach, where greater soil health scores implied better soil health. Soil health indicators were significantly affected by three but not all five soil textural groups. Evolved NH 3 and CO 2 , and potentially mineralizable N had stronger associations with each other as revealed by correlation and principal component analysis. Our results also suggested that mean separation of the tested soil health indicators was more consistent with three soil textural groups (coarse, medium, and fine) than five soil textural groups (clays, clay loams, loams, sandy loams, and sand); therefore, we recommend using three soil textural groups to develop soil health scoring functions. The findings of this study lay a groundwork for future soil health assessment involving a larger number of samples across Ontario and more soil indicators, which will facilitate the regional interpretation of soil health.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".