Development of cryopreservation methods for cherry birch (<i>Betula lenta</i> L.), an endangered tree species in Canada
Bibliographic record
Abstract
Cherry birch (Betula lenta L.) is an endangered species in Canada, with only a single natural population of 18 trees found in the Niagara region of Ontario. The tree was reportedly used for medicinal purposes by First Nations and Native American peoples. The current study describes a cryopreservation method for seeds and in vitro shoot tips of B. lenta in hopes of conserving available germplasm. Cryopreservation of mature seeds was successful, with 24% of seeds germinating in the greenhouse and 12% of seeds germinating in vitro on medium supplemented with 10 μmol·L–1 thidiazuron or 6-benzylaminopurine. Postcryopreservation regrowth of in vitro shoot tips was achieved using a droplet-vitrification protocol after preculture in 0.3 mol·L–1 sucrose for 24 h followed by osmoprotection in loading solution for 20 min and treatment with vitrification solution A3 composed of glycerol, sucrose, ethylene glycol, and dimethyl sulfoxide at 0 °C for 60 min. The highest plant regeneration (52%) was obtained after unloading (rehydration) in a medium with 0.8 mol·L–1 sucrose for 30 min. Differential scanning calorimetry analysis confirmed the absence of ice crystallization in the shoot tips during cooling and rewarming. This is the first report for successful cryopreservation of B. lenta, which will further promote initiatives for conservation of North American biocultural diversity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".