Diversity of Vegetation Types and Structure Based on the Thickness of Peat in Sebangau National Park Central Kalimantan
Bibliographic record
Abstract
The objective of this study was to examine the composition, structure, and species diversity of vegetation in Sebangau National Park based on the thickness of peat.The findings revealed that the plant species composition varied according to the peat thickness at different stages of growth.Syzigium sp.1 and Elaeocarpus parvifolius were the dominant species at the seedling level, while are Syzygiumsp.1 and Tetratomia tetradra dominated at the sapling level.Cratoxylum arborescens and Elaeocarpus parvifolius dominated at the pole level, and Cratoxylum arborescens and Diospyros bantamensis at the tree level.The species diversity of plants was high across all levels of growth, with a high category index value (3.14-3.86)for all levels except seedlings on shallow and very deep peat (medium category with an index value of 2.76 and 2.86, respectively).The species evenness index was also high (0.73-0.93) for all growth levels across all peat thicknesses except saplings on shallow peat, which had a medium category index value of 0.54.The species richness index was high (5.66-11.42)for all growth levels based on any peat thickness.The horizontal stand structure of vegetation across all peat thicknesses followed an inverted J pattern.The same index for all growth rates at all peat thicknesses ranged from low to high category with a consistent index value of approximately 44.94-85.00%.
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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) | 0.001 | 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".