SELECT THE PLUM GENITORS FOR RESISTANCE TO LATE SPRING FROST
Bibliographic record
Abstract
In recent years, climate changes more and more evident, have increased the risk of late spring frost during the flowering time with negative implications on fruit buds, flowers, fruit set and fruit production. The purpose of this paper is to evaluate some plum cultivars with different origins regarding the resistance to the late spring frosts in order to identify potential genitors for future breeding work. The research was carried out in plum demonstrative plots of Genetic - Breeding and Plant Propagation Departments by the Research Institute for Fruit Growing Pitesti, Romania, on 26 cultivars (7 from Romania, 15 from Germany, 1 from Serbia and 1 from Canada). In the last decade, spring frosts were observed in 2017 (-4,2 ºC, Aprilie, 21-22, young fruit stage), 2020 (-7,1ºC, Aprilie, 1-7, beggining of flowering), 2022 (-5,8ºC, Aprilie, 18-21, full flowering), 2023 (-6,4ºC, March, 29-30, green button) și 2025 (-5,7ºC, Aprilie, 7-11, full flowering). These climatic accidents caused damage of 80-100% in 2017, 18-90% in 2020, 15-45% in 2022, 7-65% in 2023 and 51% in 2025. Throughout this period, the least affected were 'Romanţa', 'Piteştean', 'Carpatin', 'Katinka', 'Hanka', 'Presenta', 'Elena', 'Topstar', 'Toptaste', 'Topfive', 'Tophit', 'Voyageur' and 'Stanley' cvs., the level of damage being very low (below 10%), and the percentage of fruit set being over 20%. All of these cultivars are recommended as genitors for resistance to late spring frosts in breeding programs.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".