Design and test of a generic cohort model of soil organic matter decomposition: the SOMKO model
Bibliographic record
Abstract
Abstract SOMKO is a new simulation model of soil organic matter (SOM) dynamics aimed at predicting long‐term and short‐term SOM dynamics based on a mechanistic approach focusing on microbes as the key agents of decomposition. SOM is partitioned into cohorts and chemical quality pools (classified by age and chemical composition), the microbial community processes are explicitly represented, and the C : N stoichiometric constraints are accounted for through a new mechanism of offer and demand. The analysis of model equations shows that: (1) SOM C : N cannot decrease below microbial C : N; and (2) the nitrogen limitation of decomposition depends on SOM C : N, microbial biomass and soil mineral nitrogen. First tests of the model show good qualitative behaviour for simulating the dynamics of short‐term litter‐bag type decomposition, long‐term SOM increase, pulsed mineral nitrogen production, the priming effect due to easily decomposable carbon addition, and the effects of vegetation clearance and climate change on SOM. Simulations are in good agreement with long‐term experimental data. SOMKO is an integrated component of the coupled soil–vegetation models within the ETEMA (European Terrestrial Ecosystem Modelling Activity) framework. Future extensions of this work include: (1) estimating microbial parameters from specific experiments; (2) spatial distribution of SOMKO in multistrata models; and (3) implementing nitrification/denitrification processes, phosphorus limitation and microfaunal activity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".