Soil organic carbon and total nitrogen storage under different land uses in the Naiman Banner, a semiarid degraded region of northern China
Bibliographic record
Abstract
Li, Y., Han, J., Wang, S., Brandle, J., Lian, J., Luo, Y. and Zhang, F. 2014. Soil organic carbon and total nitrogen storage under different land uses in the Naiman Banner, a semiarid degraded region of northern China. Can. J. Soil Sci. 94: 9–20. Accurate investigation of soil organic carbon (SOC) and total nitrogen (TN) storage at a regional level is important for detecting changes in the C and N sequestration and emission potentials induced by land-use and cover type changes. In a degraded semiarid region of northern China's Horqin Sandy Land, we selected 208 locations and calculated SOC and TN storage to a depth of 100 cm for the main land-use and cover types. The productive cropland on former grassland had the highest level of SOC and TN storage (6613 g C m−2 and 709 g N m−2). The corresponding storage values were 3758 g C m−2 and 402 g N m−2 in degraded grassland, 3449 g C m−2 and 373 g N m−2 in afforested dunes, 2674 g C m−2 and 320 g N m−2 in unproductive cropland on former dunes, and 1109 g C m−2 and 129 g N m−2 in sand dunes (from mobile to fixed). The average soil bulk density was highest in sand dunes, with a value of 1.59 g cm−3, and lowest in productive cropland on former grassland, with a value of 1.39 g cm−3. The conversion of severely degraded sandy land into other land-use and cover types therefore has considerable potential to partially offset the SOC and TN loss during the past century that has resulted from desertification in the Horqin Sandy Land.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".