Tree species distribution and its impact on soil properties, and nitrogen and phosphorus mineralization in a humid subtropical forest ecosystem of northeastern India
Bibliographic record
Abstract
The impact of tree species on net N and P mineralization, and soil properties beneath their canopy were studied in a subtropical evergreen broad-leaved forest of northeastern India. Four types of experimental plots were identified based on tree species dominance. The first plot was dominated by Myrica esculenta Buch.-Ham. ex D. Don., the second by Rhododendron arboreum Sm., the third by Neolitsea cassia Koster., and the fourth was a mixed-species plot. Organic carbon content (3.11%) and NH4+-N concentration (11.40 μg·g–1) in the Rhododendron plot, pH (4.64) and total N content (0.89%) in the mixed plot, available P (5.16 μg·g–1) and NO3–-N (8.63 μg·g–1) concentrations in the Neolitsea plot were significantly higher (p < 0.001) than the other plots. All these soil parameters were lowest in the Myrica plot. The net N and P mineralization rates in an annual cycle across different plots ranged between 18.83 and 22.14 μg·g–1·month–1and between 4.54 and 5.87 μg·g–1·month–1, respectively. The flux varied significantly (p < 0.001) among the plots, the lowest and highest being in the Myrica plot and mixed plots, respectively. The differences in soil properties and in net N and P mineralization among different species plots were related to litter quality and yield of the respective species as well as soil microenvironment.
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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 | 0.000 |
| 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) | 0.000 | 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".