Bibliographic record
Abstract
Poplars (Populus spp.) are among the most productive tree species in the northern hemisphere, displaying fast-growth across a wide geoclimatic range. Climate change and alterations in precipitation regimes can affect the distribution of forest trees and poplars are one of the most sensitive woody plants to water stress due to their naturally high transpiration rates; thus, drought can significantly limit the productivity of poplar trees. Cuticular wax is critical in preventing non-stomatal water loss, and its composition varies depending on tissue type, age, and species, as well as in response to diverse biotic and abiotic stresses. Genome-wide association studies can reveal a genomic response to phenotypic selection by analyzing the genetic variation occurring in the genomes of several individuals. In this study, the wax profiles of P. trichocarpa leaves were analyzed to determine the phenotypic variations between common clones grown under drought vs. non-drought conditions to examine the natural variation in wax composition. In addition, poplar clones grown in different common gardens were compared to determine whether there are inherent differences in the wax composition. Specifically, leaf wax was analyzed by GC-FID to determine the variation in the cuticular wax composition among clones and a GWAS was completed on wax traits/phenotypes. Although the total amount of wax did not change in response to drought stress, wax components such as alkenes, alcohols, and esters changed. The common garden influence, on the other hand, was found to produce considerable variations in both total wax load and wax components. Following drought treatment, GWAS revealed that two genes connected to alkanes, Potri.014G152600 (CER1) and Potri.018G072700 (FATB) which are known to play a role in wax biosynthesis. This information could be employed to select for drought-tolerant poplar genotypes in 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) | 0.000 | 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".