Characterization of 29 polymorphic microsatellite markers developed by genomic screening of Sumatran rhinoceros (Dicerorhinus sumatrensis)
Bibliographic record
Abstract
Abstract Objective The Sumatran rhinoceros is critically endangered, with fewer than 100 individuals surviving across its current range. Accurate census estimates of the remaining populations are essential for development and implementation of conservation plans. In order to enable molecular censusing, we here develop microsatellite markers with amplicon sizes of short length, appropriate for non-invasive fecal sampling. Results Due to limited sample quantity and potential lack of genome-wide diversity, Illumina sequence reads were generated from two Sumatran rhinoceros samples. Genomic screening identified reads with short tandem repeats and loci that were polymorphic within the dataset. Twenty-nine novel polymorphic microsatellite markers were characterized (A = 2.4;HO = 0.30). These were sufficient to distinguish among individuals (PID < 0.0001), and to distinguish among siblings (PID(sib) < 0.0001). Among rhinos in Indonesia, almost all markers were established as polymorphic and effective for genotyping DNA from fecal samples. Notably, the markers amplified and displayed microsatellite polymorphisms using DNA extracted from 11 fecal samples collected non-invasively from wild Sumatran rhinoceros. These microsatellite markers provide an important resource for a census and genetic studies of wild Sumatran rhinos.
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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 | 0.000 |
| 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 | 0.000 |
| 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".