The Relationship Between Feeding Patch Quality and Fodder Species of Wild Elephants in the Teknaf Wildlife Sanctuary, Cox’s Bazar, Bangladesh
Bibliographic record
Abstract
We examined the relationship between the presence or absence of elephants in patches of land and the most common ecological factors, such as fodder species, water bodies, resting places, elephant movement trails, and soil types, across ten transect sites in the Teknaf Wildlife Sanctuary (TWS), Bangladesh. By ground-truthing 360 line transects and 1080 quadrate blocks, we recorded a total of 184 fodder species, including 71 monocotyledons, 58 dicotyledons, and 55 domesticated plant species. Three categories of domesticated fodder species were recorded that consisted of 13 cultivated crops, 24 vegetables, and 18 homestead garden plants. We also applied dung-pile dissection techniques to a total of 250 dung piles between August 2018 and July 2019. Highly statistically significant differences among the abundances of different fodder species and presence of elephants were found across different transect sites. The average fodder species density was found to be 3.44 plant species per site per km2, while the elephant density was 0.63 individuals per site per km2. A significant strong correlation was found between fodder species density and the number of elephants among the transect sites (P = 0.02). The numbers of ground-recorded fodder species were higher than those found in dung piles. The presence of elephants across transect sites was influenced not only by fodder species but also by other ecological factors, such as water bodies, resting places, movement trails, and soil types.
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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) | 0.002 | 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".