QTL analysis of growth and wood chemical content traits in an interspecific backcross family of white poplar (<i>Populus tomentosa</i>×<i>P. bolleana</i>) ×<i>P. tomentosa</i>
Bibliographic record
Abstract
The genetic control of tree growth and wood chemical content traits was studied using interspecific backcross progeny between clone TB01 (Populus tomentosa × Populus bolleana) and clone LM50 (P. tomentosa). In total, 247 and 146 amplified fragment length polymorphism (AFLP) markers from genetic maps previously constructed for backcross parents LM50 and TB01 were used for the analyses of quantitative trait loci (QTL). These markers were distributed among 19 linkage groups and covered 3265 and 1992 cM in the backcross parents, respectively. A total of 32 putative QTLs, associated with five growth and chemical content traits, sylleptic branch number, sylleptic branch angle, stem volume, wood cellulose content, and wood lignin content, were detected. These QTLs were dispersed among 16 linkage groups in parent LM50 and 10 groups in parent TB01. The phenotypic variance explained by each QTL ranged from 7.0% to 14.6%. QTLs controlling sylleptic branch number and stem volume were colocalized in two linkage groups, TLG6 and TLG8, respectively. The favorable alleles were mostly from P. tomentosa, which is phenotypically superior to P. bolleana for sylleptic branch angle, stem volume, and wood chemical content traits. The favorable alleles for sylleptic branch number were from P.bolleana. These AFLP markers that were associated with the QTLs have potential use in P. tomentosa breeding programs.
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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) | 0.001 | 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".