Increasing Breeding Without Breeding (BwB) Efficiency: Full Vs. Partial-Pedigree Reconstruction in Lodgepole Pine
Bibliographic record
Abstract
The advantage of paternity assignment in assembling structured pedigree for breeding is investigated using two sampling methods; namely, family array (known maternal parent) and random offspring (unknown maternal and paternal parents) collected from an openpollinated lodgepole pine experimental population with known parents (N = 74) using nuclear and chloroplast microsatellite markers. Offspring of equivalent sample sizes representing the family array (n = 619) and random offspring (n = 635) were genotyped and subjected to partial and full pedigree reconstruction, respectively. The full pedigree reconstruction assembled substantially larger number of full-sib families than the partial (446 vs. 268) and interestingly the two methods detected equivalent amount of external gene flow to the experimental population. The superiority of the random offspring over the family array sampling in producing more full-sib families was attributed to its better representation of the parental population, as random sampling included offspring from most parents as compared to the parent-limited family array. Owing to the observed advantages, the full pedigree reconstruction could be employed as an alternative to the breeding phase commonly required in conventional breeding programs for the development of structured pedigree needed for genetic parameters estimation.
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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 | 0.002 |
| 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 | 0.001 | 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".