Cryopreservation of <i>Prunus padus</i> seeds: emphasising the significance of Bayesian methods for data analysis
Bibliographic record
Abstract
Conservation of Prunus padus L., a tree of high ecological and pharmacological importance, has been evaluated by storing seeds at subzero (−20 °C and −80 °C) and cryogenic (−196 °C) temperatures for various durations. The effect of the seed’s water content (WC) ranging from 3.5% to 21.1%, fresh weight basis, and the effect of cooling and rewarming procedures on seed viability was investigated. Emergence of seedlings was observed for 40%–55% of the noncryopreserved seeds, with no significant effect of WC. The same seedling emergence was recorded for seeds cryopreserved by direct immersion in liquid nitrogen within the WC range of 3.5%–15.0%. Seeds rehydrated above 17% WC were unable to tolerate cryopreservation. Seedling emergence was not affected by cooling regime but decreased by 10% after stepwise rewarming compared with rapid rewarming in a water bath or on air. No reduction in seedling emergence was recorded after storage at −20 °C, −80 °C, and −196 °C for 1 h, 1 week, and 1 month. We recommend seed storage at subzero or cryogenic temperatures as an effective conservation option for P. padus and possibly other Prunus species. We also demonstrated high effectiveness and reliability of Bayesian statistical methods for analyzing binomial data such as the data obtained in seed conservation experiments.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".