Male reproductive success and pedigree error in red spruce open-pollinated and polycross mating systems
Bibliographic record
Abstract
Open-pollinated and polycross mating systems are widely used in forest genetics and breeding to quickly, simply, and inexpensively generate progenies assumed to be related as half-sibs (coefficient of relationship, r = 0.25) from a random mating population. However, nonrandom mating, such as unequal male reproductive success (RS) or selfing, can increase the genetic correlation among offspring, and thus, genetic variance and heritability are overestimated. Conversely, pedigree errors will cause additive genetic variance and heritability to be underestimated. Unequal male reproductive success and three types of potential pedigree errors (volunteers, mishandled maternal identities, and foreign pollen) were detected in operational open-pollinated and polycross red spruce ( Picea rubens Sarg.) progeny tests, through paternity testing using microsatellite (simple sequence repeat) DNA markers. The potential impact of unequal RS and pedigree errors on quantitative genetic parameters is discussed. Paternity and parentage analyses could be used to reconstruct the pedigree of any plantation consisting of sibships, where candidate parents (e.g., members of seed orchard) can be identified. This offers an alternative to traditional progeny testing for estimation of quantitative genetic parameters.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".