Linking Elephant Movement Patterns to Vegetation Dynamics in Bukit Barisan Selatan National Park
Bibliographic record
Abstract
The importance of understanding elephant movement patterns in relation to vegetation conditions in their home range is for sustainable habitat management strategies. This study aims to map the movement patterns of elephants across different habitat vegetation within their home range and to correlate these patterns with vegetation metrics such as species richness, diversity, and the Importance Value Index, as well as elephant feeding preferences. The study was conducted by placing 100 plots in the home range based on the intensity of the movement of elephants in various types of vegetation; primary forest, secondary forest, shrubs, and gardens in Bukit Barisan Selatan National Park. Movement data of elephants taken from GPS Collars available in WWF Lampung. Non-parametric statistical analysis using SPSS to test the significant relationship between variables ( 2 ). The results showed that the value of species richness, diversity, and evenness in a primary forest is high. The intensity of elephant movement is lowest in primary forest when compared to secondary forest, shrubs and gardens. The implications of this research are the importance of maintaining forests for the protection and development of elephant populations and the need to map areas frequently visited by elephants.
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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".