Structural genomic variations and their effects on phenotypes in <i>Populus</i>
Bibliographic record
Abstract
Abstract DNA copy numbers have recently emerged as an important new marker system. In the absence of a contiguous reference genome, alternative detection systems such as the comparative hybridization method have been used to detect copy number variations (CNVs). With the advent of chromosome-level resolved reference genomes based on the incorporation of long-read sequencing and powerful bioinformatics pipelines, comprehensive detection of all structural variations (SVs) in the poplar genome is now within reach. Gene CNVs and their inheritance are important because they can cause dosage effects in phenotypic variations. These are potent genetic markers that should be considered in complex trait variation such as growth and adaptation in poplar. SVs such as CNVs could be used in future genomic selection studies for poplar, especially in cases when heterosis increases hybrid performance (hybrid vigor). This Chapter reports recent findings on SVs in natural populations of Populus spp. as well as on artificially induced SVs in poplar to understand their potential importance in generating a considerable amount of phenotypic improvement. The Chapter concludes with an outlook on the future implementation of knowledge on SVs in poplar crop breeding.
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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 | 0.000 | 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) | 0.002 | 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".