Aggregate dispersion for soil organic matter fractionation: a case study from Québec, Canada
Bibliographic record
Abstract
Soil organic matter fractionation is often used in studies aiming at better understanding soil organic matter (SOM) dynamics. Particle-size fractionation using a 53 µm threshold is a simple and effective method to isolate SOM pools with contrasting properties. Although numerous methods of aggregate dispersion exist, little is known about their efficacy to completely disperse aggregates. With the goal of developing a standardized method for Québec, this study compared three widely used aggregate dispersal methods on 30 soils from across the province, covering a wide range of textures and organic carbon content. The three tested methods were (1) ultrasonication at 450 J mL −1 (US450), (2) shaking with glass beads in water (H 2 O), and (3) shaking with glass beads in a sodium hexametaphosphate solution (SHMP). Although the mass and carbon recovery were high for all three methods, the US450 method showed a higher mass of the >53 µm fraction and a lower mass of the <53 µm fraction compared to the other two methods, which would reflect an incomplete dispersion. This hypothesis was supported by scanning electron microscopy images. The carbon and nitrogen concentrations in both fractions were also lower for the US450 method in the >53 µm fraction for heavy-textured soils and higher in the <53 µm fraction for all soils. Since no significant differences were observed regarding fractions composition indicators between the H 2 O and SHMP methods, the H 2 O method was identified as the most appropriate fractionation method, as it is simple and does not require the use of chemical products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".