A modeling approach to quantifying soil macroaggregate dynamics
Bibliographic record
Abstract
While several researchers have suggested that soil aggregate turnover is a significant control on organic matter dynamics, the quantification of soil aggregate dynamics has yet to be achieved. Quantification of soil aggregate turnover is essential to testing any hypothesis concerning the relationship between aggregate turnover and organic matter dynamics. The goal of the current work was to propose a modeling approach to the quantification of soil macroaggregate dynamics. The approach taken was to define model compartments representing water-stable soil aggregate size fractions and describing the flows between compartments using first-order kinetics. Soil aggregate data from a 2-yr field study on two contrasting soils were used to calibrate the model and yielded soil aggregate mean residence times ranging from 4 to 95 d, where aggregate dynamics were generally two to three times more rapid in a Gray Luvisol compared to a Black Chernozem. The model was subsequently used to predict the distribution of applied tracer spheres in water-stable aggregate size fractions from an initially free state. The models closely predicted the Dy "mean weight diameters" (Dy-MWD) after two growing seasons. While the models have several limitations, they offer the first attempt to quantitatively describe soil macroaggregate dynamics, which is essential to predicting the response in organic matter dynamics to changes in aggregate dynamics. Key words: Soil aggregation, macroaggregate turnover, tracer, model
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".