Fine root contribution to the soil carbon stock of an agroforestry system in a Caatinga-Atlantic Forest transition zone
Bibliographic record
Abstract
The objective of this work was to evaluate the distribution of fine roots and its influence on the soil organic carbon stock, at a depth of 20 cm, in a Grevillea robusta and Coffea arabica agroforestry system. The study was conducted in an agroforestry system established 15 years ago in a transition area of Caatinga and Atlantic Forest biomes in Brazil. G. robusta trees representing the most frequent diameter class were selected, and three distances of these trees (0, 0.75 and 1.50 m) and two soil collection depths (0–10 and 10–20 cm) were defined. The root samples were scanned and quantified using a software program. There was a general predominance of roots with a diameter of 0.6 mm at the shortest distance from the surface layer, while there was a predominance of roots with a diameter of 0.4 mm in the 10–20 cm layer. The root carbon stock at a distance of 0.75 m was higher at a depth of 0–10 cm (0.60 Mg ha-1). The soil organic carbon stock also showed higher results in the 0–10 cm layer compared to the 10–20 cm layer, although with significant variation only in the distance of 1.5 m. There was a higher concentration of fine roots in the topsoil, probably influenced by a greater availability of water and nutrients from plant residues. The soil carbon stock is not closely related to root density or root carbon stock. The data presented in this study do not provide a definitive conclusion.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".