Distinguishing Forest and Savanna African Elephants Using Short Nuclear DNA Sequences
Bibliographic record
Abstract
A more complete description of African elephant phylogeography would require a method that distinguishes forest and savanna elephants using DNA from low-quality samples. Although mitochondrial DNA is often the marker of choice for species identification, the unusual cytonuclear patterns in African elephants make nuclear markers more reliable. We therefore designed and utilized genetic markers for short nuclear DNA regions that contain fixed nucleotide differences between forest and savanna elephants. We used M13 forward and reverse sequences to increase the total length of PCR amplicons and to improve the quality of sequences for the target DNA. We successfully sequenced fragments of nuclear genes from dung samples of known savanna and forest elephants in the Democratic Republic of Congo, Ethiopia, and Namibia. Elephants at previously unexamined locations were found to have nucleotide character states consistent with their status as savanna or forest elephants. Using these and results from previous studies, we estimated that the short-amplicon nuclear markers could distinguish forest from savanna African elephants with more than 99% accuracy. Nuclear genotyping of museum, dung, or ivory samples will provide better-informed conservation management of Africa's 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".