Interspecific hybridizations of <i>Fraxinus</i> L. (<i>F. mandshurica</i> × <i>F. americana</i> and <i>F. mandshurica</i> × <i>F. velutina</i>) and heterosis analysis and selection of F1 progenies
Bibliographic record
Abstract
The interspecific hybridizations of Fraxinus mandshurica Rupr. × Fraxinus americana L. (MA) and Fraxinus mandshurica × Fraxinus velutina Torr. (MV) were conducted to solve the problems of poor cold adaption associated with the introduction of Fraxinus in Heilongjiang province. High-voltage electrostatic field (HVEF) treatment to pollen was performed to overcome the prefertilization barriers. The hybrids adapted more strongly and grew better than the pure species (heterosis over higher parent (HHP) of the 9-year volume index was 5.5% for MA and 23.1% for MV) in Heilongjiang province. HVEF treatment greatly improved the number of seeds (0.25- to 5.52-fold) and seedlings (1.63- to 8.71-fold) of the hybrids. Additionally, three excellent female parents (nos. 15, 16, and 17) and seven hybrid combinations of MA (D94, D70, and D100) and MV (D103, D116, D105, and D104) with excellent growth traits were selected. The HHPs of volume index were 39.1%–112.5% for selected hybrids. Additionally, predicted growth trends of the hybrids showed that the hybrids will maintain a 7.7% to 9.3% height advantage over F. mandshurica through ages 10 to 15 years in Mao Ershan. These findings will accelerate the genetic breeding process of Fraxinus species.
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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 | 0.001 |
| 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 | 0.001 |
| 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".