Carbon and Nitrogen Stock Under Different Types of Land Use in a Seasonally Dry Tropical Forest
Bibliographic record
Abstract
The aim of this study, was to analyse the effect of cover vegetation change on stocks of Total Organic Carbon (ST.TOC) and Total Nitrogen (ST.TN) in soils of a Seasonally Dry Tropical Forest in the Brazilian semi-arid region. The study was carried out on three farms located on a typical Orthic Chromic Luvisol in an SDTF. Soil samples were collected from trenches, 70 × 70 cm in size, in the 0-10, 10-20, 20-30, 30-40, 40-60 and 60-80 cm layers, under four types of land use: dense Caatinga (DC), open Caatinga (OC), agriculture (AG) and pasture (PA). The following attributes were evaluated: bulk density, Total Organic Carbon (TOC), Total Nitrogen (TN), ST.TOC and ST.TN. The data were compared using the Mann-Whitney test (p ≤ 0.05). Hierarchical Grouping Analysis (HGA) was used to understand the behaviour of the attributes evaluated between cover vegetation types. Using HGA resulted in the formation of three distinct groups for the types of land use under investigation. The highest mean values for ST.TOC (11.29 Mg ha-1) and ST.TN (3.36 Mg ha-1) were found in CD and CA. The changes in land use in the SDTF had an effect on ST.TOC and ST.TN. It is therefore necessary to adopt strategies and strengthen conservation practices in areas of agricultural and pasture, and reduce the process of degradation and further the process of recovery in these areas. Such action will reduce the loss of C and N, and increase the levels and stocks of TOC and TN.
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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 | 0.000 | 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".