Ecosystem-level carbon storage and its links to diversity, structural and environmental drivers in tropical forests of Western Ghats, India
Bibliographic record
Abstract
Tropical forests are rich in biodiversity with great potential for carbon (C) storage. We estimated ecosystem-level C stock using data from 70 forest plots in three major forest types: tropical dry deciduous (TDD I and TDD II), tropical semi-evergreen (TSE I and TSE II) and tropical evergreen forests (TEF I, TEF II and TEF III) of Kanyakumari Wildlife Sanctuary, Western Ghats, India. The average C stock in these forests was 336.8 Mg C/ha, of which 231.3, 3.0, 2.4, 15.2 and 84.9 Mg C/ha were stored in woody vegetation, understorey, litter, deadwood and soil respectively. The live vegetation, detritus and soil contributed 65.5%, 5.5% and 29% respectively to the total ecosystem-level C stock and distributed in forest types in the order: TEF III > TEF II > TEF I > TSE I > TDD II > TSE II > TDD I. The plant diversity, structural attributes and environmental factors showed significant positive correlations with C stocks and accounted for 6.7, 77.2 and 16% of variance. These findings indicate that the tropical forests in the Western Ghats store large amount of C, and resulting data are invaluable for planning and monitoring forest conservation and management programs to enhance C storage in tropical forests.
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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 | 0.001 | 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".