Mineralization of active soil organic carbon in particle size fractions of a Brookston clay soil under no-tillage and mouldboard plough tillage
Bibliographic record
Abstract
Soil organic matter is a heterogeneous mixture of organic substances with different compositions and stabilities. To enhance soil organic carbon (SOC) sequestration, it is helpful to understand the distribution of SOC among the soil particle size fractions, the stabilities of the SOC within each fraction, and the influence of management practices, such as tillage, on SOC mineralization. Hence, the objectives of this study were to determine the distribution and mineralization rate of active SOC in the sand (53-2000 µm), silt (2-53 µm) and clay (<2 µm) size fractions of a Brookston clay soil under a corn-soybean rotation and three tillage scenarios. The tillages included long-term (24 yr) mouldboard plough (MP83), long-term (24 yr) no-till (NT83) and short-term (10 yr) no-till (NT97). Bulk soil from the top 10 cm was dispersed into sand, silt and clay size fractions by applying ultrasound energy (750 J mL-1) to a 1:4 soil:water suspension. The sand, silt and clay fractions of the three tillages were aerobically incubated at 20°C and 30% moisture (wt/wt), and the CO2-C emissions were measured over 103 d. The SOC contents followed the order clay > silt > sand for all three tillages. On a whole-soil basis and averaged over the tillage treatments, 55.8% of the SOC was in the clay size fraction, 37.2% was in the silt fraction and 7.0% was in the sand fraction. Carbon mineralization rate in the incubated samples decreased over time, and was related to both particle size fraction and tillage practice. There was very good agreement between a first-order decay model (Ct= C1 + C0 (1-e-kt)) and measured SOC mineralization rates for all size fractions and tillages. The C mineralization data indicated that: (1) the SOC in the clay and sand fractions was more decomposable than the SOC in the silt fraction; and (2) the SOC associated with the clay and silt fractions was more readily decomposable under no-till than under mouldboard plough.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".