The impact of land conversion from boreal forest to agriculture on soil health indicators
Bibliographic record
Abstract
Climate change is creating opportunities for agricultural expansion northward into the boreal forest. Converting forested land to agricultural land generally results in significant losses of organic matter (OM), which can impact soil health (SH). The objectives of this study were to assess the effects of land conversion on indicators of SH and to use the Comprehensive Assessment for Soil Health (CASH) framework to integrate measures of these indicators into a score to evaluate land conversion effects. Total carbon and nitrogen were also measured in this study. Soils (0–5 and 5–15 cm) were collected from six dairy farms near Thunder Bay, ON, that included a mature forest, a field converted from forest to agriculture <10 years ago and a field converted from forest to agriculture >50 years ago. Land conversion resulted in significant declines in permanganate oxidizable carbon, wet aggregate stability, soil respiration, and concentrations of OM, autoclave citrate extractable protein, total nitrogen, and total carbon. Lower CASH scores in the soils converted to agriculture are interpreted to represent a decline in SH but the scores, along with soil organic matter (SOM) concentrations, remain high (CASH = 80; OM = 6%). There was no effect of time since conversion, suggesting that any degradation to SH happens quickly and is closely tied to declines in SOM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".