Droplet Digital <scp>PCR</scp> Provides Highly Sensitive and Accurate Opsin Gene <scp>SNP</scp> Detection From Wild Primate Fecal Samples
Bibliographic record
Abstract
ABSTRACT Evaluating field‐sourced samples with poor‐quality and low‐quantity DNA, like animal feces, presents significant challenges in the field of molecular biology. Nonetheless, recent innovations in PCR technology are promoted as effective tools to overcome many of these issues. Here, we evaluate the efficiency of droplet digital PCR (ddPCR) as a method for color vision assessment from feces of white‐faced capuchins ( Cebus imitator ) and report frequencies of alleles and genotypes in a wild population. The sex‐linked color vision polymorphism of monkeys in the Americas is driven by single nucleotide polymorphisms (SNPs) in opsin genes at up to three tuning sites. DNA was extracted from fecal samples collected from 211 wild capuchins (53.1% males) in Sector Santa Rosa, Costa Rica: 56 were evaluated with ddPCR, 24 with both ddPCR and Sanger sequencing, and 141 with Sanger sequencing (historical dataset). The same opsins and genotypes were derived for each monkey using Sanger and ddPCR; however, the latter method was far more sensitive and required far fewer samples to reach a definitive genotype. Overall, the most frequent phenotypes were red and green/red. The distribution of genotypes was: Females ( N = 99): green/red (35.4%), red/red (33.3%), green/yellow (14.1%), yellow/red (12.1%), yellow/yellow (4.0%), and green/green (1.0%); Males ( N = 112): red (60.7%), yellow (23.2%), and green (16.1%). Overall, ddPCR was a reliable method for evaluating color vision noninvasively in wild capuchins with the advantage of excellent sensitivity and high‐throughput. ddPCR is highly robust to PCR inhibitors and can be potentially used to identify other disease‐related SNP mutations noninvasively in wild animals.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".