Development of Microsatellite Loci Exhibiting Reverse Ascertainment Bias and a Sexing Marker for use in Emperor Geese ( <i>Chen Canagica</i> )
Bibliographic record
Abstract
The Alaskan population of Emperor Geese ( Chen canagica) nests on the Yukon–Kuskokwim Delta in western Alaska. Numbers of Emperor Geese in Alaska declined from the 1960s to the mid-1980s and since then, their numbers have slowly increased. Low statistical power of microsatellite loci developed in other waterfowl species and used in previous studies of Emperor Geese are unable to confidently assign individual identity. Microsatellite loci for Emperor Goose were therefore developed using shotgun amplification and next-generation sequencing technology. Forty-one microsatellite loci were screened and 14 were found to be polymorphic in Emperor Geese. Only six markers – a combination of four novel loci and two loci developed in other waterfowl species – are needed to identify an individual from among the Alaskan Emperor Goose population. Genetic markers for identifying sex in Emperor Geese were also developed. The 14 novel variable loci and 15 monomorphic loci were screened for polymorphism in four other Arctic-nesting goose species, Black Brant ( Branta bernicla nigricans), Greater White-fronted ( Anser albifrons), Canada ( B. canadensis) and Cackling ( B. hutchinsii) Goose. Emperor Goose exhibited the smallest average number of alleles (3.3) and the lowest expected heterozygosity (0.467). Greater White-fronted Geese exhibited the highest average number of alleles (4.7) and Cackling Geese the highest expected heterozygosity (0.599). Six of the monomorphic loci were variable and able to be characterised in the other goose species assayed, a predicted outcome of reverse ascertainment bias. These findings fail to support the hypothesis of ascertainment bias due to selection of microsatellite markers.
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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 | 0.001 | 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".