A Model to Predict Soil Aggregate Stability Dynamics following Organic Residue Incorporation under Field Conditions
Bibliographic record
Abstract
Our ability to predict the effects of various organic amendments on soil aggregate stability is limited due to the complexity of the biological, chemical, and physical mechanisms involved. Based on previous experimental results, this study developed a model (Pouloud) to predict the dynamics of aggregate stability following the incorporation of various organic residues under field conditions. Following Monnier's conceptual model and previously published data, a lognormal function is first used to describe changes in aggregate stability after organic inputs under laboratory conditions. Using principal component analysis, the parameters of the lognormal function are associated with the biochemical characteristics of the organic products such as water‐extractable polysaccharide, cellulose and hemicellulose, and lignin contents. To simulate aggregate stability dynamics under field conditions, the effects of soil moisture, soil temperature, and N availability are taken into account by specific functions obtained from the literature. When model simulations were compared with experimental results under field conditions, variations in aggregate stability were generally well reproduced. The sensitivity of the model to climate variations and organic residue characteristics was tested. Soil N availability and the substrate lignin content are major factors that influence the prediction of aggregate stability dynamics. Our results suggest that prediction of aggregate stability dynamics under field conditions using organic substrate characteristics and simple climatic data is possible. More work is required to test the model and broaden its applicability to other soil and climatic conditions.
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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 | 0.000 | 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".