Factors that influence measurements of sweet cherry (<i>Prunus avium</i>) flower bud cold hardiness obtained using differential thermal analysis
Bibliographic record
Abstract
Differential thermal analysis (DTA) is a technique commonly used to evaluate the cold hardiness of plant organs that supercool as a means for cold survival. The aim of this study was to evaluate the effect of different pretest bud storage conditions, cooling rates, and bud excision techniques on dormant sweet cherry flower bud low temperature exotherms (LTEs) measured using DTA. Furthermore, this study compared cold hardiness estimates made using DTA and controlled freezing tests. We determined that buds stored at warmer temperatures (12.5 °C and room temperature) for 2–6 h prior to DTA or transported to the lab in a moist environment underwent biologically relevant changes in their apparent sensitivity to cold, as indicated by LTEs produced at warmer temperatures. The DTA cooling rate also significantly affected LTEs, with faster cooling resulting in the production of LTEs at warmer temperatures. Overall, LTEs were comparable among buds with varying amounts of plant material remaining attached to the bud base. It is important to note that the region directly subtending the primordia was always left intact on the buds being compared. This study demonstrated that overall, DTA and controlled freezing tests resulted in comparable measures of cold hardiness. The findings presented in this study are pertinent to researchers interested in conducting cold hardiness measurements in sweet cherry and highlight that consistency in DTA pretest conditions and bud preparation are required to achieve reliable LTE results that can be compared among studies.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".