Leaf and root production, decomposition and carbon and nitrogen fluxes during stand development in tropical moist forests, north-east India
Bibliographic record
Abstract
In the present study we investigated the production and decomposition of leaves, branches and roots in two moist regenerating tropical forests (5 and 15 years old; RF-5 and RF-15 respectively) and a natural forest (NF) in north-east India. Total litter input increased during vegetation succession (759, 1089 and 1284 g m–2 year–1 in RF-5, RF-15 and NF respectively), whereas the contribution of soft litter decreased sharply. Decomposition over 450 days indicated significant seasonal (P < 0.001) patterns in mass loss of litter components, with greater rates during the wet period. Soil CO2 efflux was strongly seasonal. C stock loss followed patterns similar to those of mass loss, whereas N increased initially, followed by its gradual release. Rainfall explained 74–90% of the variability in mass loss rates. Concentrations of cellulose and N were significantly positively correlated with mass loss at an early stage of decomposition (r = 0.54–0.65, P < 0.05), whereas lignin : N and C : N ratios were negatively correlated with mass loss at later stages. Regenerating forests adapted ecosystem-level strategies that induced early leaf fall to reduce soil water loss, increase organic matter return to the soil and conserve N through immobilisation during the process of decomposition to speed up vegetation succession in the regenerating forest.
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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".