Soil organic carbon and land use: Processes and potential in Ontario's long-term agro-ecosystem research sites
Bibliographic record
Abstract
Congreves, K. A., Smith, J. M., Németh, D. D., Hooker, D. C. and Van Eerd, L. L. 2014. Soil organic carbon and land use: Processes and potential in Ontario's long-term agro-ecosystem research sites. Can. J. Soil Sci. 94: 317-336. Soil organic carbon (SOC) is crucial for maintaining a productive agro-ecosystem. Long-term research must be synthesized to understand the effects of land management on SOC storage and to develop best practices to prevent soil degradation. Therefore, this review compiled an inventory of long-term Ontario studies and assessed SOC storage under common Ontario land management regimes via a meta-analysis and literature review. In general, greater SOC storage occurred in no-till (NT) vs. tillage systems, in crop rotation vs. continuous corn, and in N fertilizer vs. no N fertilizer systems; however, soil texture and perhaps drainage class may determine the effects of tillage. The effect on SOC storage was variable when deeper soil depth ranges (0-45 cm) were considered for NT and rotational cropping, which suggests an unpredictable effect of land management on SOC at depths below the plough layer. Therefore, researchers are encouraged to use the presented inventory of nine long-term research sites and 18 active experiments in Ontario to pursue coordinated studies of long-term land management on SOC at depths extending below the plough layer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".