A SIMPLE SOIL ORGANIC-MATTER MODEL FOR BIOMASS DATA ASSIMILATION IN COMMUNITY-LEVEL CARBON CONTRACTS
Bibliographic record
Abstract
Soil carbon (C) sequestration has been proposed as a transitional win-win strategy to help replenish organic-matter content in depleted agricultural soils and counter increases in atmospheric greenhouse gases. Data assimilation and remote sensing can reduce uncertainty in sequestered C mass estimates, but simple soil organic carbon (SOC) models are required to make operational predictions of tradeable amounts over large, heterogenous areas. Our study compared the performance of RothC26.3 and a reduced compartmental model on an 11-year fertilizer trial in subhumid West Africa. Root mean square error (RMSE) differences of 0.05 Mg C/ha between models on total SOC predictions suggest that for contractual purposes, SOC dynamics can be simulated by a two-pool structure with labile and stable components. Faster (seasonal) and slower (semicentennial and beyond) rates can be approximated by constants as instantaneous and infinite decay. In these systems, simulations indicate that cereal residue incorporation holds most potential for mitigation of transient C loss associated with recent land conversion to agriculture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".