Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada
Bibliographic record
Abstract
Adoption of soil health indicators to assess physical, biological, and chemical properties involves adapting their interpretation for a specific region using scoring functions. Accordingly, we used data provided from 1166 soil samples distributed between fine-, medium-, and coarse-textured soils, collected in agricultural areas across the province of Quebec, Canada, and analyzed for 15 soil health indicators. Scoring functions were calculated according to the means and standard deviations obtained for each soil health indicator by textural group. Three scoring types were used: “more-is-better”, “less-is-better”, and “optimum-is-best”. The results showed that 12 indicators were significantly influenced by soil texture and need separate scoring functions, except for wet aggregate stability, penetration resistance of the surface hardness (0–15 cm), and pH. This led to the development of one to three scoring functions for each soil health indicator. Correlation analysis between soil health indicators was also investigated to better understand relationships between soil physical, biological, and chemical properties. We observed that soil biological indicators were moderately to strongly correlated with each other ( r = 0.59–0.74) and with soil physical indicators ( r = 0.60–0.76). Overall, the results of this study led to the development of new scoring functions based on soil texture to interpret soil health indicators objectively and accurately for the benefit of Quebec farmers and agricultural stakeholders. The findings of this study demonstrated the need to adapt scoring functions to better account for the impact of regional factors on agricultural soils for the interpretation of soil health indicators.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".