Cross Species use of Human Microarray Genotyping Technology for Bornean Orangutan (Pongo pygmaeus) SNP Discovery
Bibliographic record
Abstract
Previous genetic studies of orangutans (Pongo spp.) have relied mainly upon mitochondrial DNA or microsatellite short tandem repeats (STR) for genomic genotyping analysis. Scientists have yet to take advantage of the genetic closeness of the great apes to humans for genomic analysis by using advanced techniques available for human genotyping. To genotype orangutans at Tanjung Puting National Park, we developed a novel combination of a methyl-based magnetic enrichment capture of genomic fecal DNA with genotyping on a human targeted single nucleotide polymorphism (SNP) microarray, and compared this to additional microsatellite (STR) micro-capillary genotyping. We successfully isolated 125 known human genomic SNP loci (0.08% of those targeted) which hybridized orangutan DNA on the human targeted Illumina Infinium QC array. We estimated genetic diversity and relatedness (r) using three estimators for a total of 32 (21 female and 9 male) wild orangutans at the Camp Leakey study site. Average TrioML relatedness within the sample, estimated from our combo SNP/STR dataset, was at a range consistent with half and first cousins (r = .082). All sampled males and females had relatives within the study site indicating we have verified a local, closely related community of wild orangutans at Camp Leakey.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".