Perceptions of farmers in Bangladesh to Asian Elephants (Elephas maximus) as an Agricultural Pest in
Bibliographic record
Abstract
We explored the degree to which Bangladeshi farmers perceive Asian elephants (Elephas maximus) as agricultural pests, as related to the type of farming and other demographic profile of the farmers. We analysed the size and cropping patterns of farms raided by wild elephants, the extent and nature of crop loss, the months and crop-raiding time; and the size of elephants’ herds that caused crop-raiding incidents. The average loss of entire crops increased with distance from the park up to 300 m and then decreased with greater the distance. The greatest loss due to crop raiding was associated with specific crops. Farmers incurred the greatest mean losses in terms of cost from like rice, vegetables, banana, and teak. The highest proportion of small losses occurred during the early evening, while the greatest financial losses occurred during late evening. Wild elephants raided crops throughout most of the year, but the greatest loss and cost were incurred during the monsoon season. The proportion of crops lost varied with the herd size of elephant responsible for crop-raiding and the duration of crop-raiding. Differences were found in the views of farmers regarding the perceptions towards elephant as pest. Considering crop-raiding elephant herd as pest has been given different views by the farmers based on their financial/comfortable status.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".