Common Medicinal Plants Species Found at Burned and Unburned Areas of Klias Peat Swamp Forest, Beaufort, Sabah Malaysia
Bibliographic record
Abstract
The aims of this study is to survey the abundance and diversity of medicinal plants found in burned and unburned areas of klias peat swamp forest, Beaufort. There are 16 plots established with the size of 25m x 25m for each plot with total area of 1 ha. All the plots were established using random sampling method and Simpson’s Index and Important Value (IV) were used to determine the diversity and abundance of the species. The result of the study shows that 11 species have been found in burned area while 10 species at unburned area. The most common medicinal plant species are identified as Stenochlaena palustris, Melastoma malabathricum, Lygodium flexuosum, and Clidemia hirta. The most abundant medicinal plant species found in burned area was Stenochlaena palustris with 185 percent (%). While in unburned area, the most abundant medicinal plants were Hedychium longicornutum and Lygodium flexuosum with 55 percent each. Simpson’s Index is higher with 0.55 in burned area compared in unburned area with only 0.14. Where when the value of Index increases, the diversity will decrease and this proved that diversity of medicinal plants in unburned area was slightly higher than the burned area. This situation might be caused by the previous land clearing due to burning and small scales landuses activities at the edges of Klias peat swamp forest. More research is needed in order to gain more precise data
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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".