Comparing predictions of long-term soil carbon dynamics under various cropping management systems using K-model and CENTURY
Bibliographic record
Abstract
There is a strong demand for accurate estimates of long-term changes in soil organic carbon (SOC) with different agricultural practices under different soil and climate conditions. A process and analytic model, K-model, including a non-compartmental algorithm of soil carbon decomposition, was developed to simulate the changes of SOC under different cropping and soil management practices. This study evaluates the performance of K-model by comparing its predictions on SOC with measurements and predictions of CENTURY model, which is widely used for the similar purposes. Both K-model and CENTURY can predict the dynamics of SOC when site-specific soil and climate data are used to initialize simulations. Very similar annual carbon decomposition rates were simulated by the single carbon pool K-model and the 3-carbon pool CENTURY model. However, compared with experimental measurements of SOC, K-model produces relative smaller errors than CENTURY (<0.1 kg C m-2 vs. 0.08-0.48 kg C m-2, and within
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".