Bibliographic record
Abstract
The distribution of the Asian elephant (Elephas maximus) has been limited to the remaining discontinuous forests, mainly in the South and Southeast Asia. A global number of wild Asian elephants have been declining due to habitat loss, forest fragmentation, and anthropogenic disturbance. Acquiring information of wild populations is important for effective conservation and management plan. This article reviews the applications of noninvasive genetic method as a tool for studying wild Asian elephants. Noninvasive genetic method has been introduced to the field of wildlife conservation for more than two decades. The method provides reliable information of a population and facilitates investigation of genetic effects on small and fragmented populations. Various DNA markers for the Asian elephant, those include mitochondrial DNA, microsatellite DNA, and sex determination markers, have been developed and used to study wild elephant populations across the distribution range. Most of the studies revealed the issues of low genetic diversity in the small populations and interruption of gene flow among the fragmented populations. Tracking of ivory poaching has not yet been done in the Asian elephant. It could be carried out if a reference genetic database of the natural populations is available. Noninvasive genetic method has been proved to be a promising tool for conservation of the wild Asian elephants. Transboundary collaboration would give hope for a successful long-term conservation of this charismatic species in their natural habitats.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".