Macroaggregate persistence: Definition and applications to describe soil surface dynamics
Bibliographic record
Abstract
Macroaggregates (diameter > 0.25 mm) help the soil surface to resist erosive forces, but their contribution to soil surface stability changes with time because macroaggregate formation and disintegration is a dynamic process. Surface macroaggregates can be visualized by advanced image analysis, a non-invasive method to track aggregates. The objective of this study was to develop a mathematical method to describe the spatial and temporal dynamics of surface macroaggregates observed in digital images. We define aggregate persistence as the ability of aggregates to remain in a pre-determined spatial unit throughout a given time span. The first index explains how many aggregates with the same size distribution remain on a soil surface area through time, which we call the Grouped Aggregate Persistence Index (GAPI). The proportion of individual aggregates with the same size, shape and location at the beginning and end of a measurement period is the Individual Aggregate Persistence Index (IAPI). We calculate the GAPI and IAPI for macroaggregates on the surface of a clay agricultural soil, as an example. Photographs of the soil surface (55 cm 2 ) are analyzed with a customized MATLAB program that uses the watershed method to calculate the macroaggregate size distribution for the GAPI and identify the size, shape and location of macroaggregates for the IAPI. These persistence indices are a non-destructive way to describe dynamic changes in macroaggregates at the soil surface, which is complementary to other methods that visually evaluate the soil structure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".