Bibliographic record
Abstract
Noninvasive genotyping has not gained wide application, due to the notion that it is unreliable, and also because remedial measures are time consuming and expensive. Of the wide variety of noninvasive DNA sources, dung is the most universal and most widely used in studies. We have developed collection, extraction, and amplification protocols that are inexpensive and provide a high level of success in amplifying both mitochondrial and nuclear DNA from dung. Here we demonstrate the reliability of genotyping from elephant dung using these protocols by comparing results from dung-extracted DNA to results from blood-extracted DNA. The level of error from dung extractions was only slightly higher than from blood extractions, and conducting two extractions from each sample and a single amplification from each extraction was sufficient to eliminate error. Di-, tri-, and tetranucleotide loci were equally reliable, and low DNA quantity and quality and PCR inhibitors were not a major problem in genotyping from dung. We discuss the possible causes of error in genotyping with particular reference to noninvasive samples and suggest methods of reducing such error.
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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 | 0.102 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".