Effect of elevation on soil quality under bamboo (Bambusa teres Buch.-Ham. ex Munro) stands outside forest areas in Eastern Nepal
Bibliographic record
Abstract
Bamboo dynamics in non-forest areas remain relatively underexplored, despite over 50 % of the global bamboo population being found in degraded, marginal or agricultural lands outside forests. To address this, we investigated soil quality dynamics under isolated bamboo stands ( Bambusa teres ) across three elevation regions: lower (0–400 m), middle (400–800 m), and higher (800–1200 m) in Katari, Udayapur, Nepal. Stratified sampling, followed by purposive sampling, was used to account for elevation variation and bamboo's scattered distribution. A total of thirty 100 m 2 circular plots (10 per elevation stratum) were sampled at two soil depths (0–15 cm and 15–30 cm) to assess soil quality, using various indicators based on published literature from Nepal. At middle elevation, organic carbon, nitrogen and potassium were significantly higher at 0–15 cm, while phosphorus and pH were higher at 15–30 cm (p ≤ 0.05). A fair soil quality rating (SQI: 0.48 –0.57) was observed in the study area. Elevation significantly (p ≤ 0.05) affected SQI at 0 –15 cm depth, with higher SQI at middle elevation (0.57) and lower SQI at lower elevation (0.48). For effective bamboo management and land-use planning, it is important to consider elevation-specific zoning. Middle and higher elevations should be prioritized for bamboo plantations, incorporating management activities and agroforestry integration to enhance soil productivity. Further studies with larger samples and broader geographic coverage, incorporating additional soil indicators and environmental variables is recommended. • B. teres has scattered distribution outside forest area in Nepal. • Limited understanding about bamboo soil quality dynamics in non forest area. • No significant differences in soil quality across studied soil depths. • Fair soil quality (SQI; 0.48–0.57) was observed despite no management intervention. • Bamboo agroforestry with management at middle elevations could enhance soil productivity.
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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 | 0.000 | 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".