Using a sequential density and particle-size fractionation to evaluate carbon and nitrogen storage in the profile of tilled and no-till soils in eastern Canada
Bibliographic record
Abstract
Tillage influences the retention and storage of organic matter (OM) in soil. We used a sequential density and particle-size fractionation to evaluate the total quantity and distribution of organic carbon (C) and nitrogen (N) in the profile of soils under mouldboard ploughing and no-till. Cores (0-60 cm) from four long-term tillage studies (in Prince Edward Island, Quebec, and two sites in Ontario) on soils of different textures, from sandy loam to clay were collected and divided into six depth increments. Four soil OM fractions were isolated on the basis of size and density from each sample. At two sites the standing stocks of C and N were larger in tilled than in no-till soil profiles at depths slightly greater than plough depth; tillage effects were also evident at a third site, where the mass of C in a layer close to the depth of ploughing was greater in tilled soils. Ploughing also had a substantial and consistent positive effect in all four soils on the quantity of C (and in some cases N) protected within aggregates, particularly at or near the bottom of the plough layer, and sometimes in surface soil layers. We attribute differences observed in whole soil C and N due to tillage mostly to differences in mineral-associated OM, particularly in soils with heavier textures, but the other size and density fractions could, together, account for as much as 25% of the difference. Because the C capacity level was not reached in heavy-textured soils, these soils show greater potential for further accumulation of C in the soil profile than coarse-textured soils, especially near the bottom of the plough layer.Key words: Light fraction, soil organic matter, soil tillage, physically-protected organic matter, no-till, soil texture
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".