Autonomy: An Indicator of Wellbeing in Rewilded Asian Elephants Connected to Karen Communities in Thailand
Bibliographic record
Abstract
Abstract Considering the unique interactions between wild and captive Asian elephants, and conservation efforts’ predominant focus on wild populations, we advocate for a rewilding model that enhances captive elephants’ wellbeing by facilitating more “wild” states. We collaborated with Karen elephant-keeping communities in Thailand, integrating expertise of Karen mahouts and communities’ cultural values concerning elephants and forest conservation. With data collected across seasons and involving six Karen-owned elephants in native forests, mixed-model regression analyses were utilized to develop a wildness index of wellbeing, revealing that increased interaction with natural environments, social engagements with other elephants, and self-directed activities significantly boost autonomy. Results suggest captive elephants can exhibit increased wildness and autonomy within natural forest settings, supporting the potential for rewilding practices that foster both elephant wellbeing and conservation of cultural traditions in human-elephant ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".