Carbon sequestration potential of cropland reforestation on the northern slope of the Tianshan Mountains
Bibliographic record
Abstract
The effect of land-use changes on soil carbon stocks has been an increasing concern in the context of global climate change. Through natural reforestation programs, abandoned cropland holds the potential of sequestering soil organic carbon (SOC) if the original forest could be recovered. In this study, we initially delineated the potential distribution of forest species on the north slope of the Tianshan Mountains using species distribution models. We then estimated the corresponding sequestration potential of SOC in the area delineated for reforestation. The deforestated area of a Picea schrenkiana forest converted to cropland (PSC) was defined by the potential and actual distributions of forest and cropland. The SOC contents of the forest and cropland soils were obtained through field sampling and laboratory analysis. We found that the area of the PSC was 26.77 × 105 ha, and the SOC loss (per unit area) derived from the conversion of forestland to cropland was 171.70 ± 28.20 Mg ha−1. The total SOC loss from the study area was 459.70 ± 75.49 Tg. This result implies that continuing the reforestation programs being implemented in the study area would increase SOC by the same amount. Additionally, we also estimated the total amount of carbon that would be sequestered in the aboveground and underground forest biomass on former cropland.
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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 | 0.000 |
| 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) | 0.000 | 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".