Genetic assessment of subspecies composition in bean goose (Anser fabalis) harvest in Sweden, Finland and Estonia
Bibliographic record
Abstract
Abstract Bean goose ( Anser fabalis) harvest in Europe consists of two subspecies, whose conservation statuses are different. However, the proportions of each subspecies in hunting bags are unknown. We studied the subspecies composition among harvested bean geese in Sweden, Finland and Estonia by sequencing a short mitochondrial DNA (mtDNA) region (210 bp). The proportion of taiga bean geese ( A. f. fabalis ) over two hunting seasons was 94% in Sweden, but only 5.8% and 11% in Estonia and southeastern Finland, respectively. The majority of harvested bean geese in Estonia and southeastern Finland were tundra bean geese ( A. f. rossicus ), and hence the results show that the Finnish spatio-temporal harvest regulations have successfully managed to focus the harvest mostly to the abundant tundra bean goose. We also detected mitochondrial heteroplasmy, i.e. multiple mtDNA variants within some of the individuals. In addition, we discovered a few exceptional individuals with an mtDNA haplotype belonging to eastern taiga bean goose ( A. f. middendorffii ) or greater white-fronted goose ( A. albifrons ), which could be hybrids between bean goose subspecies or interspecific hybrids. Hybrid individuals are a problem to this type of method. We also noted that it was not possible to distinguish bean geese and pink-footed goose ( A. brachyrhynchus ). Our derived method is more cost-efficient than previously used molecular methods, and could be used to monitor bean goose hunting bag in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".