Labile soil carbon fractions as indicators of soil quality improvement under short-term grassland set-aside
Bibliographic record
Abstract
Grassland set-asides (GLSA) are fields that are taken out of intensive annual crop production and seeded with a mixture of grasses and legumes for one to four years to improve soil quality. The objectives of this study were to evaluate (i) the relationships among soil organic carbon (SOC), permanganate oxidisable C (POXC), dilute-acid extractable polysaccharides (DAEP) and aggregate stability to determine if they may be used as proxies for one another, (ii) whether these indicators could be used to predict aggregate stability, (iii) if differences in soil quality after short-term GLSAs, detected with aggregate stability, could instead be detected with POXC or DAEP and (iv) potential use of diffuse Fourier transform spectroscopy (FT-MIR) to predict POXC, DAEP and aggregate stability in the Fraser River Delta region of British Columbia, Canada. There were strong relationships among SOC, POXC and DAEP, but the relationship between DAEP and SOC (R2 = 0.60, P < 0.0001) was less strong than that observed between POXC and SOC (R2 = 0.71, P < 0.0001). All three soil C fractions were significantly predicted with the 2–6 mm aggregate size fraction but the correlations for DAEP (R2 = 0.43) and POXC (R2 = 0.36) were stronger than that for SOC (R2 = 0.29). Predictions of soil quality indicators using FT-MIR produced R2 = 0.92 for POXC, R2 = 0.93 for DAEP and R2 = 0.62 for the 2–6 mm aggregate size fraction. These results suggest that FT-MIR holds promise as a low-cost method to determine labile soil C fractions that are better proxy soil quality indicators for aggregate stability than SOC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".