Landscape‐Level Assessment of Topographic Influences on Organic Carbon Storage in Forests of Far Western Nepal
Bibliographic record
Abstract
ABSTRACT Carbon sequestration significantly aids in mitigating climate change, with its spatial distribution greatly influenced by topographical factors. However, data on organic carbon distribution and its interaction with topographic factors inside the forest of the Far Western Region of Nepal are limited. Therefore, this study aims to analyze forest carbon stock variation under different topographic variables (physiographic region, aspect, and slope) in Far‐western Nepal. In this study, stratified systematic cluster sampling was adopted with elevation, aspect, and slope as strata. A total of 181 circular plots were used for dendrometric measurements and soil sample collection. Within each plot, diameter at breast height and height of each tree (diameter at breast height ≥ 5 cm) were measured for biomass carbon assessment. Composite soil samples (0–30 cm) from each soil pit within a plot were collected for determining soil organic carbon stock. Physiographic region‐wise, our study reported the highest mean aboveground carbon (174.04 ± 29.75 ton ha−1) and belowground carbon (34.044 ± 5.95 ton ha−1) and soil organic carbon stock (150.62 ± 11.02 ton ha−1) in the Mountain and High Himal region. The East aspect exhibited the highest aboveground carbon (125.9 ± 22.34 ton ha−1) and belowground carbon (27.54 ± 3.44 ton ha−1) stocks, while the North aspect showed the highest soil organic carbon stock (96.85 ± 8.82 ton ha−1). Organic carbon stocks declined with steeper slopes, with the (0–10)° slope category recording the highest aboveground organic carbon (135.17 ± 17.87 ton ha−1), belowground carbon (27.03 ± 3.57 ton ha−1), and soil organic carbon (107.14 ± 12.51 ton ha−1) stocks. Conversely, the (30–40)° slope category exhibited the lowest organic carbon stocks across all pools. This study's findings will support accurate monitoring, reporting, and verification (MRV) processes for initiatives like reducing emissions from deforestation and forest degradation (REDD+) and enhance credibility on United National Framework Convention on Climate Change (UNFCCC) reporting on a national scale. The design and application of site‐specific management activities to optimize organic carbon storage are recommended due to the observed variability of organic carbon stock with topographic factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".