Long-term S-fertilization increases carbon sequestration in a sulfur-deficient soil
Bibliographic record
Abstract
Giweta, M., Dyck, M. F., Malhi, S. S., Puurveen, D. and Robertson, J. A. 2014. Long-term S-fertilization increases carbon sequestration in a sulfur-deficient soil. Can. J. Soil Sci. 94: 295–301. We analyzed the change in total soil organic carbon (SOC) in a long-term fertilization experiment (1980–2008) in a wheat–oat–barley–hay–hay rotation system at the University of Alberta Breton Classical plots. Soil samples were taken in 1980, 1990, 1998, 2003 and 2008 from plots that were fertilized with NPKS and NPK. The objective was to compare the relative effect of S fertilization on the SOC stocks in a S-deficient soil. Long-term S fertilization resulted in an increasing trend in soil organic carbon concentrations over 28 yr when N, P and K supply were adequate. The change in SOC with time was significantly different (P<0.05) between the two treatments. Annual application of NPK in combination with S resulted in an increased accumulation of SOC at a rate of 0.11 Mg C ha−1 yr−1 in the 0- to 15-cm depth over NPK alone. Our results suggest that long-term S fertilization in an S-deficient soil helped to sequester C in the soil.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".