A study of introduced apple cultivars according to the main components of winter hardiness by simulating damaging factors under controlled conditions
Why this work is in the frame
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Bibliographic record
Abstract
Background. Most of the plantings of fruit crops in Russia are located in the zone of risky agriculture. In the European part of Russia, in winter, fruit crops are affected by the impacts of weather conditions (spring frosts, droughts, early frosts, low-temperature stress, a short growing season, and thaws). Frosts cause 98% of the damage to fruit trees.Methods. One-year-old branches were frozen in a Japanese Espec PSL-2KPH climate chamber after prehardening under –5°C and –10°C for 5 days, and damaging factors of the winter period were simulated.Results. The bioresource collection of the All-Russian Research Institute of Fruit Crop Breeding (VNIISPK) contains 730 appletree cultivars from various domestic and foreign institutions. Apple cultivars from Ukraine, Belarus, Latvia, Moldova, USA, France, Czech Republic, Sweden and Canada were analyzed for frost resistance components. The resistance of plants to early frosts of–25°C without hardening and after hardening in early winter (Component I) showed that the main tissues (bark, cambium and wood) suffered minor damage in all studied cultivars. In cv. ‘Belarusskoye Sladkoye’, the damage to the bark scored 2.3 points. Among the studied apple cultivars whose one-year-old branches were frozen at –38°C and –40°C (Component II), ‘Coremolda’ (Moldova) showed the highest frost resistance to the negative mid-January temperature of –38°C (damage to the buds and main tissues scored 0.3–1.0 points). Under–40°C (Component II), ‘Coremolda’ (Moldova) and ‘Aivaris’ (Latvian breeding) demonstrated bark, cambium and wood resistance with damages at the level of 2.0 points. These cultivars can be used in breeding programs as sources of frost resistance. Freezing of one-year-old branches under –25°C after a 3-day artificial thaw at +2°C revealed bud and tissue resistance in the American cv. ‘Red Free’ and in cv. ‘Coremolda’ (Component III).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it