Genetic variation of leaf traits related to productivity in a<i>Populus deltoides</i>×<i>Populus nigra</i>family
Bibliographic record
Abstract
In breeding and selection, two of the main goals of hybridization are to combine favourable traits from different species and to obtain high hybrid vigor (or heterosis). The objectives of our study were (1) to determine which leaf traits are most closely linked to growth in a cross between Populus deltoides Bartr. ex Marsh. and Populus nigra L. and (2) to estimate the relevance of this cross for selection of highly productive genotypes. To achieve these objectives, 26 poplar F1hybrids and their parents were studied during their second growing season in central France. Tree growth (i.e., growth rates of stem height, circumference, and volume) was monitored during 1 month, and leaf traits (i.e., increases in number of leaves, maximum individual leaf area, specific leaf area, petiole length, and dry mass, leaf carbon and nitrogen contents, and internode length) were estimated at the end of the 1-month period. Growth traits were tightly correlated to most of the leaf traits. More precisely, it appeared that stem volume growth rate can be decomposed into two single leaf characteristics: maximum individual leaf area and leaf increment rate. All traits showed moderate values of broad-sense heritability. Heterosis as well as coefficients of genetic variation were also modest.
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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 | 0.000 |
| 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".