Diverse developmental mutants revealed in an activation-tagged population of poplarThis article is one of a selection of papers published on the Special Issue of Poplar Research in Canada.
Bibliographic record
Abstract
We have produced the largest population of activation-tagged poplar trees to date, approximately 1800 independent lines, and report on phenotypes of interest that have been identified in tissue culture and greenhouse conditions. Activation tagging is an insertional mutagenesis technique that results in the dominant upregulation of an endogenous gene. A large-scale Agrobacterium -mediated transformation protocol was used to transform the pSKI074 activation-tagging vector into Populus tremula × Populus alba hybrid poplar. We have screened the first 1000 lines for developmental abnormalities and have a visible mutant frequency of 2.4%, with alterations in leaf and stem structure as well as overall stature. Most of the phenotypes represent new phenotypes that have not previously been identified in poplar and, in some cases, not in any other plant either. Molecular analysis of the T-DNA inserts of a subpopulation of mutant lines reveal both single and double T-DNA inserts with double inserts more common in lines with visible phenotypes. The broad range of developmental mutants identified in this pilot screen of the population reveals that it will be a valuable resource for gene discovery in poplar. The full value of this population will only be realized as we screen these lines for a wide range of phenotypes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".