Preliminary estimates of genetic parameters and familial selection for non-native poplars show good potential for genetic gains on growth, cold hardiness, trunk quality and Sphaerulina musiva susceptibility
Bibliographic record
Abstract
Abstract Genetic parameters for growth, trunk quality, and susceptibility to frost and Sphaerulina musiva attack were estimated from 34 half-sib families of hybrid poplar from the crossing of non-native parents, Populus maximowiczii A. Henry, and Populus trichocarpa Torr. & Gray, 3 and 6 years after planting. The use of spatial analysis proved to be the best method for quantitative growth data. The proportion of the among-family variance to the total (phenotypic) variance as well as the high heritabilities of growth and susceptibility to frost and Spaherulina musiva showed a high potential for selection for these traits while the quality traits were under low genetic control. Some families showed gains for several traits, suggesting the possibility of developing a selection index to obtain superior families that show gain for not only growth but quality and adaptive traits as well. Type B correlations were high, suggesting that families responded in the same way regardless of the site. High type A correlation between growth traits at 3 and 6 years showed early selection potential, although these relationships should be confirmed with future measurements to evaluate this effect at maturity. These results can be integrated into the strategy for improving hybrid poplar parental populations and, in the longer term, will make it possible to optimize the selection of individuals with traits of interest for the operational deployment of hybrid poplar clones.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".