Stratification ratio of soil organic carbon as an indicator of carbon sequestration and soil quality in ecological restoration
Bibliographic record
Abstract
The stratified distribution of soil organic carbon (SOC) provides a potential means of eliminating the difference in soil background for understanding its response to ecological processes. We assessed the feasibility of SOC stratification ratio (SR) as an index to estimate the dynamics of SOC sequestration and soil quality during ecological restoration. SOC, total nitrogen, and available nitrogen contents were measured at restored sites containing three vegetation types with different stand ages and slope gradients, also at sites with three kinds of agricultural management on the hilly Loess Plateau, China. SR and SOC density (SOCD) showed a consistently significant trend of linear increase along the revegetation chronosequences. The proportion of the annual increase rates of SR to SOCD were approximately 1:15 and 1:5 for SR1 (0–5:5–10 cm) and SR2 (0–5:20–30 cm), indicating that SR of the shallow soil layers (0–10 cm) could estimate SOC accumulation to a depth of 30 cm. SRs significantly increased owing to the ecosystem restorations. Also, SRs could discriminate the difference in SOC sequestration and soil quality between vegetation types. SR, however, could not precisely indicate the variation of SOC sequestration and soil quality under different agricultural management. The study suggested that SR was an efficient indicator of the dynamics of SOC sequestration and soil quality, and an SR2 (0–5:20–30 cm) >2 indicated a distinct improvement of SOC sequestration and soil quality in ecological restoration on the hilly Loess Plateau.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".