A nondestructive method for obtaining maternal DNA from avian eggshells and its application to embryonic viability determination in herring gulls (<i>Larus argentatus</i>)
Bibliographic record
Abstract
Many avian studies, aimed at collecting samples for genetic analysis, rely upon invasive procedures involving the capture and handling of parents and their offspring. Our goal was to develop a nondestructive method for sampling maternal DNA that would not require blood collection from the mother. Herein, we describe a method for isolating genomic DNA from eggshell powder, obtained by filing the outer shell of an avian egg. Comparison of microsatellite profiles, obtained from genomic DNA found within eggshell matrices and their corresponding parents, verified the presence of maternal DNA in the eggshell matrix in 100% of the herring gull nests assessed (n= 11). In addition, the microsatellite profiles of eggshell DNA were identical among eggs from the same clutch. The ability to rapidly obtain a DNA sample from an avian eggshell in a noninvasive manner could aid in a wide range of genetic sampling studies, and in this study, we provide one potential application of this finding: assessing the fertilization status of nonviable herring gull (Larus argentatus) eggs from the Laurentian Great Lakes. Detection of fertilization was successful as the microsatellite profiles of eggshell powder (maternal only) and the fertilized embryonic contents of those eggs did not match. Ideally, the application of such an approach will help to discriminate unfertilized eggs from embryos aborted early in development and provide insights into avian reproductive health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".