Parentage and grandparentage assignment with known and unknown matings: application to Connecticut River Atlantic salmon restoration
Bibliographic record
Abstract
Large-scale use of parentage assignment of stocked Connecticut River, U.S.A., Atlantic salmon (Salmo salar) fry to determine stocking tributary of emigrating smolts and returning adults requires both parentage assignment and grandparentage assignment. Using computer simulations, we explored the levels of genetic diversity necessary to provide adequate parentage and grandparentage assignment. When matings were known, as in a hatchery, 95% correct grandparentage assignment typically required at least twofold more alleles per locus than did 95% correct parentage assignment. Parentage assignment with unknown matings (as in the wild when parental genotypes can be assayed) required an intermediate number of alleles per locus and grandparentage assignment with unknown matings required the greatest genetic diversity. Computer simulations using empirical parental genotypes (14 loci) and known P1 matings indicated that the genetic diversity of Connecticut River Atlantic salmon broodstock is sufficient to assign all F1 and 97% of F2 offspring to the correct set of two parents or four grandparents, respectively. Grandparentage assignment with unknown P1 matings was ineffective (assignment success = 20%). These results indicate that the combination of parentage and grandparentage assignment can be used as a non-lethal, permanent, heritable "mark" to identify stocking tributary or other management directed characteristics of stocked fry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".