Impact of Sampling Depth on Differences in Soil Carbon Stocks in Long‐Term Agroecosystem Experiments
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Bibliographic record
Abstract
The depth of sampling has recently been highlighted as critical to making accurate measurements of changes in SOC stocks. This paper aimed to determine the effects of land management changes (LMC) on soil organic carbon (SOC) by re‐sampling long‐term agoecosystem experiments (LTAEs) across Canada using identical sampling and laboratory protocols. The impact of sampling depth on the monitoring of LMC‐induced differences in SOC stock in LTAEs in Canada, and the implications on statistical power and sampling design, were assessed. In most cases, four cores would be suitable for detecting a significant difference in SOC stock of 5 Mg ha −1 at 95% confidence for LMCs in western Canada. The impact of eliminating fallow on SOC stocks was typically restricted to the surface 15 cm. The impact of perennial forages on the average cumulative SOC was sufficiently large to be detectable at all sampling depths (to 60 cm). In three of the six LTAEs sampled in western Canada comparing conventional tillage to no‐till, there was a significantly greater SOC storage in the 0‐ to 30‐depth than the 0‐ to 15‐cm depth, suggesting that sampling below 15 cm could be necessary. The same comparisons in eastern Canada suggested that sampling often must exceed the 30‐cm depth to account for any changes in SOC due to moldboard plow tillage. Nonetheless, there was little evidence to suggest that increasing sampling intensity or sampling deeper would improve the ability to detect a difference in SOC stocks for this LMC.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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