Improved estimation of aquaculture associated European introgression in a captive breeding program for endangered Atlantic salmon
Bibliographic record
Abstract
Abstract The rapid, range-wide decline in Atlantic salmon, Salmo salar, populations is well documented and has led to establishment of captive rearing and breeding programs in order to preserve populations. However, recovery potential may be limited by the inclusion of non-local genotypes, which can be both difficult to detect and quantify. In the genetically unique Inner Bay of Fundy population located in Canada, three Live Gene Bank programs have been established to aid recovery of this endangered conservation unit. Evidence of aquaculture associated non-local (i.e., European) introgression had previously been detected using small panels of microsatellite markers with limited power. Here we show how advances in sequencing and machine learning technologies can support a conservation program. We used machine learning and a corresponding panel of 301 SNPs to estimate individual-level proportions of European ancestry. To assess the degree of introgression in each program and to assess changes over time, fish were randomly selected across several program generations. Estimates were validated by genotyping a subset of individuals on a 220 K SNP array and using established admixture methods. Of the 1741 fish analyzed, only 48 were found to have European ancestry greater than the detection threshold. We found the amount of European ancestry was previously overestimated, and that very few wild-collected founder individuals had large proportions of European ancestry. Moreover, because European ancestry was introduced to Bay of Fundy populations via introgression from aquaculture escapees, these values represent the minimum amount of aquaculture introgression in these captive populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".