Interspecific variation in leaf litter production, decomposition, and nitrogen and phosphorus loss from decomposing leaves in a humid subtropical forest ecosystem of northeastern India
Bibliographic record
Abstract
Studies providing direct experimental evidence of species impact on litter dynamics in forest ecosystems are limited. The decomposition processes in subtropical forests are also poorly understood. We studied variation in quality and quantity of leaf litter production, decomposition, and N and P loss from decomposing foliar litter in three tree species as well as a mixed-species plot in a subtropical broad-leaved forest of northeastern India. The annual leaf litter production was highest in Rhododendron arboreum Sm. (7293 kg·ha–1·year–1) followed by Myrica esculenta Buch.-Ham. ex D. Don (6902 kg·ha–1·year–1), mixed plots (6808 kg·ha–1·year–1), and Neolitsea cassia (L.) Kosterm (6299 kg·ha–1·year–1). The annual N and P inputs through litter were highest in the mixed plot (N, 111.0 kg·ha–1·year–1; P, 4.8 kg·ha–1·year–1) and lowest in the Rhododendron plot (N, 65.6 kg·ha–1·year–1; P, 2.9 kg·ha–1·year–1). The highest decay rate was recorded for Neolitsea (k = 0.89) and lowest for Myrica (k = 0.53) litter. The rate of N loss was highest for Neolitsea (kN= 1.39) and lowest for Myrica (kN= 0.68) species, and P loss was in the order of mixed (kP= 1.02) > Neolitsea (kP= 0.88) > Rhododendron(kP= 0.84) > Myrica (kP= 0.62). Acid-insoluble residue, which indicates lignin content and P-related litter chemistry, were correlated with the differential decomposition rates and nutrient loss pattern among the species.
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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 | 0.001 |
| 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".