Reliable Noninvasive Genotyping: Fantasy or Reality?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it