Spatial Distribution of Density Fractions of Soil Organic Carbon at a Sloping Farmland of a Black Soil
Bibliographic record
Abstract
A typical undulating farmland in Black Soils region of Northeast China was taken as a case study to analyze the impact of soil erosion and deposition process on density fractions of soil organic carbon based on measurement of light and heavy organic carbon in different geomorphic positions, which were under various levels of erosion. The results showed that the carbon concentration in light fraction decreased with increasing in soil depth. In the surface 20 cm soil, light fraction carbon (LF-C) ranged from 45.3 mg kg-1 to 65.8 mg kg-1, which account for 2.91-5.87% of total soil organic carbon (TOC). The amount of LF-C, HF-C and TOC at the shoulder-slope where is subject to serious erosion was 27.4, 17.2 and 18.1% lower than those of summit where erosion is weak. Relevant to total soil organic carbon, the LF-C is more sensitive to soil erosion and deposition process, but the decrease of TOC is the combination of the decrease of LF-C and HF-C. The change of LF-C/TOC is similar as LF-C, indicating that soil erosion can preferentially cause the loss of LF-C. Accordingly, LF-C can be a good indicator of the early change of soil equality as affected by soil erosion. At foot-slope and toe-slope positions, C re-deposition has lead to significantly higher of LF-C compared than that at shoulder-slope. LF-C and microbial biomass carbon (MBC) showed the similar pattern along the slope, indirectly indicating that the dependence of soil microbes on the light-fraction of organic matter. The fate of the LF-C deposited in the foot-slope and toe-slope positions should be studied further, and HF-C is still the dominating fraction in deposited area.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".