<i><scp>P</scp>opulus trichocarpa</i>cell wall chemistry and ultrastructure trait variation, genetic control and genetic correlations
Bibliographic record
Abstract
The increasing ecological and economical importance of Populus species and hybrids has stimulated research into the investigation of the natural variation of the species and the estimation of the extent of genetic control over its wood quality traits for traditional forestry activities as well as the emerging bioenergy sector. A realized kinship matrix based on informative, high-density, biallelic single nucleotide polymorphism (SNP) genetic markers was constructed to estimate trait variance components, heritabilities, and genetic and phenotypic correlations. Seventeen traits related to wood chemistry and ultrastructure were examined in 334 9-yr-old Populus trichocarpa grown in a common-garden plot representing populations spanning the latitudinal range 44° to 58.6°. In these individuals, 9342 SNPs that conformed to Hardy-Weinberg expectations were employed to assess the genomic pair-wise kinship to estimate narrow-sense heritabilities and genetic correlations among traits. The range-wide phenotypic variation in all traits was substantial and several trait heritabilities were > 0.6. In total, 61 significant genetic and phenotypic correlations and a network of highly interrelated traits were identified. The high trait variation, the evidence for moderate to high heritabilities and the identification of advantageous trait combinations of industrially important characteristics should aid in providing the foundation for the enhancement of poplar tree breeding strategies for modern industrial use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".