In vitro conservation of American elm (<i>Ulmus americana</i>): potential role of auxin metabolism in sustained plant proliferation
Bibliographic record
Abstract
An efficient procedure for the conservation of mature American elm ( Ulmus americana L.) trees that have survived the epidemics of Dutch elm disease and are potential sources of disease resistance is reported. The model utilizes in vitro proliferation of fresh and dormant buds from mature trees for cloning nearly 100 year old American elm trees. The key factors that influenced sustained growth and multiplication included optimization of culture process and auxin metabolism in the source tissue. Blocking the action of endogenous auxins through the addition of antiauxin in the proliferation medium was crucial for high multiplication rate and optimum shoot development. Addition of antiauxin also mitigated the decline in productivity observed with multiple subcultures, which will enable long-term conservation of selected germplasm. The most effective medium for long-term proliferation contained 5.0 µmol/L p-chlorophenoxyisobutyric acid with 2.2 µmol/L benzylaminopurine and 0.29 µmol/L gibberellic acid. Medium with 2.5 µmol/L indole-3-butyric acid was the best for rooting microshoots (89%). Rooted plantlets were readily acclimatized to the greenhouse environment with a 90% survival rate. The strategy developed for American elm will aid in increasing multiplication of resistant clones, facilitate long-term conservation of elite genotypes, and also provide an approach to improve conservation of other endangered tree species.
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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 | 0.000 | 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 | 0.001 |
| 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".