Efficiencies of Clonally Replicated and Seedling Testing for Spruce Breeding and Deployment Strategies
Bibliographic record
Abstract
Abstract Genetic gains based on a genetic test using clonal replicates were compared to those based on a test using seedlings at the same gene diversity and testing effort levels using POPSIM™ Simulator. Three testing and deployment strategies targeting for white spruce (P. glauca [Moench] Voss) and black spruce (P. mariana (Mill.) B.S.P.) in New Brunswick were compared: seedling test with clonal seed orchard deployed as seedlings (CSO_ST), clonally replicated test with clonal seed orchard deployed as seedlings (CSO_CRT), and clonally replicated test deployed as a clone mix (MVF). The breeding populations (BP) were formed by balanced within-family selection and the production populations (PP) were selected by strong restriction on relatedness, i.e., no parent in common. Compared to the seedling test, the clonally replicated test resulted in faster accumulation of additive effects but quicker loss of additive variance in the BP, and this is particular true in the case of lower narrow-sense heritability or less non-additive genetic variance. The quicker loss in BP additive variance was overcompensated for by its faster accumulation in BP additive effect, resulting in higher gain in the clonally replicated test based PPs. Compared to the CSO_ST, the gain superiority of the CSO_CRT increased with generations, decreasing narrow-sense heritability or reducing the amount of non-additive variance. Implementing MVF was the most effective in terms of gain in most simulated cases and its superiority over the CSO_ST increased with generations, decreasing narrowsense heritability, or increasing non-additive genetic variance. Overall results demonstrated significant advantages of using clonally replicated test both for BP advancement and PP selection in most of the scenarios, suggesting that clonally replicated test should be incorporated into current spruce breeding strategies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".