Using 2 Genetic Markers to Discriminate Among Canada Goose Populations in Ohio
Bibliographic record
Abstract
Abstract: Canada goose ( Branta canadensis ) harvest management depends on reliable estimates of harvest composition, and established genetic methods provide an alternative to traditional methods. We expanded upon previous genetic studies by comparing the utility of 6 nuclear microsatellite loci and mitochondrial (mtDNA) control region sequences for discriminating among giant ( B. c. maxima ) and interior ( B. c. interior ) populations in Ohio (USA) Canada goose harvests at both individual and population levels. Subspecies and populations exhibited greater differentiation in mtDNA ( F ST = 0.202) than microsatellites ( F ST = 0.021), as would be expected based on differences in effective population size. Neither microsatellites nor mtDNA alone were sufficient for estimating harvest composition at the subspecies or population level in simulations and empirical blind tests using individuals of known origin; however, a combined microsatellite + mtDNA dataset yielded accurate and precise harvest derivations at the subspecies level. Both population‐level mixed stock analysis and individual‐level assignment tests provided accurate results, but a large proportion of birds could not be assigned with confidence at the individual level. We applied mixed stock analysis and the combined microsatellite + mtDNA dataset to Ohio's 2003–2004 harvest and found that interior populations accounted for 4.9% (95% CI = 1.7–8.0%) of the statewide early season and 9.3% (95% CI = 6.9–11.6%) of the regular and late‐season harvested sample. These results suggest that maximum likelihood harvest derivations are highly dependent on the choice of genetic markers. Studies should only employ markers that exhibit sufficient variation and have been shown through simulations and empirical testing to accurately discriminate among the subspecies or management populations of interest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".