Distribution of soil organic carbon in the light and heavy fractions for six shelterbelt species and their adjacent agricultural fields in Saskatchewan.
Bibliographic record
Abstract
Agroforestry systems play an important role in the sequestration of carbon (C) to reduce atmospheric carbon dioxide (CO2) levels. However, the extent of long-term C sequestration will depend on physical stabilization of the sequestered C. This study determined the influence of six major shelterbelt species on soil organic carbon (SOC) distribution in the light- and heavy-density fractions of bulk soil compared with adjacent agricultural fields. Soil samples were collected from the shelterbelts and adjacent agricultural fields and were separated into light and heavy fractions using sodium iodide solution (NaI, density = 1.6 g cm−3) and analyzed for their organic C stocks. Both the light and heavy fractions to a 50 cm soil depth contained higher SOC stocks for the shelterbelts (21 and 91 Mg C ha−1, respectively) compared with the adjacent agricultural fields (14 and 81 Mg C ha−1, respectively). Most SOC added at the 0–10 cm soil depth was in the form of labile light fraction (92%), whereas heavy fraction contributed to 70% of the increase in the SOC stocks at the 10–30 cm soil depth. Increase in light-fraction SOC stocks was higher for coniferous species compared with hardwood species, and accounted for 48%–50% and 28%–31% of the increase in SOC stocks for coniferous and hardwood shelterbelts, respectively. This trend was attributed to the differences in the amount and quality of litter between coniferous and hardwood species.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".