Freezing injuries to flower buds and their influence on yield of apricot (<i>Prunus armeniaca</i>L.) and peach (<i>Prunus persica</i>L.)
Bibliographic record
Abstract
Szymajda M., Pruski, K., Żurawicz, E. and Sitarek, M. 2013. Freezing injuries to flower buds and their influence on yield of apricot ( Prunus armeniaca L.) and peach ( Prunus persica L.). Can. J. Plant Sci. 93: 191–198. Each spring, for 3 consecutive years, 2009–2011, the degree of freezing injury to overwintering flower buds of selected cultivars and clones of apricot and peach was evaluated at the Dąbrowice Research Institute of the Horticulture E$xperimental Orchard (District of Skierniewice, Poland). Apricot flower buds were damaged less severely than those of peach during the winter of 2009/2010 when the lowest temperatures (maximum drop in temperature) coincided with the full dormancy stage of the trees. Conversely, peach flower buds were less damaged than buds of apricot when the maximum drop in temperature occurred after the late winter thaw in both 2008/2009 and 2010/2011. Under Polish climatic conditions, peach proved to be more reliable than apricot in consistency to produce fruit. Results have shown that the most hardy apricot and peach cultivars can develop very strong winter hardiness allowing the trees to survive temperatures as low as −28°C, as long as there are no severe temperature fluctuations during late winter.
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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) | 0.001 | 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".