Data from: Recombinant DNA modification of gibberellin metabolism alters growth rate and biomass allocation in Populus
Bibliographic record
Abstract
Overexpression of genes that modify gibberellin (GA) metabolism and signaling have been previously shown to produce trees with improved biomass production but highly disturbed development. To examine if more subtle types of genetic modification of GA could improve growth rate and modify tree architecture, we transformed a model poplar genotype (Populus tremula × P. alba) with eight genes, including two cisgenes (intact copies of native genes), four intragenes (modified copies of native genes), and two transgenes (from sexually incompatible species), and studied their effects under greenhouse and field conditions. In the greenhouse, four out of the eight tested genes produced a significant and often striking improvement of stem volume, and two constructs significantly modified the proportion of root or shoot biomass. Characterization of GA concentrations in the cisgenic population that had an additional copy of a poplar GA20-oxidase gene showed elevated concentrations of 13-hydroxylated GAs compared to wild-type poplars. In the field, we observed growth improvement for three of the six tested constructs, but it was significantly greater for only one of the constructs, a pRGL:GA20-oxidase intragene. The greenhouse and field responses were highly variable, possibly to due to cross-talk among the GA pathway and other stress response pathways, or due to interactions between the cisgenes and intragenes with highly similar endogenes. Our results indicate that extensive field trials, similar to those required for conventional breeding, will be critical to evaluating the value and pleiotropic effects of GA-modifying genes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.041 |
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".