Predictive soil health indicators across a boreal forest to agricultural conversion gradient
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A changing climate offers new opportunities to expand agriculture in northern latitudes, and understanding forest‐to‐agriculture land conversion impacts is critical to ensure soil sustainability. Using the Comprehensive Assessment of Soil Health (CASH) framework, we identified a minimum suite of indicators with little collinearity to reliably predict soil impacts during the conversion of boreal forest to agriculture and a time since conversion gradient (forest, <10 years, >10 and <50 years, and >50 years since conversion). We sampled paired forest and agricultural sites and used multiple linear regression to assess 16 indicators and found four‐ and six‐indicator models predicted the CASH score with varying but reasonable accuracy depending on conversion class. Organic matter, water aggregate stability, and pH were consistent predictors across all classes, as well as one or more micronutrients. The CASH framework appears to be more suitable for agricultural soils and as time since conversion proceeds.
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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 | 0.001 |
| 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) | 0.000 | 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 it