Estimation of soil organic carbon under different vegetation types on a hillslope of China’s northern Loess Plateau using state-space approach
Bibliographic record
Abstract
Soil organic carbon (SOC) plays a critical role in revegetation of semi-arid areas. The accurate estimation of SOC under various land-use types is fundamental to sustain ecosystem productivity. Thus, the dominant factors of the spatial distribution of SOC in shallow soil layers were determined at hillslope scale. The state-space modeling approach was used to quantify the relationship between SOC stock and land-use type, soil properties, topographic features, and fine root biomass (FRB) at 0–20 and 20–40 cm soil layers of a hillslope on the Loess Plateau. The best state-space models explained more than 96% of the variations in SOC stocks on the hillslope. The best multivariate state-space models including land-use type, FRB, soil pH, and total nitrogen were optimal for 0–20 and 20–40 cm soil layers. Land-use type was the dominant factor for identification of localized variation in SOC in the 0–40 cm soil layer. The results underscored the importance of land-use type in SOC variation on the hillslopes of the Loess Plateau. It also provided a useful insight into the accurate estimation of SOC using state-space modeling approach driven by other easily obtainable variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".