Effect of temperature on pollen tube kinetics and dynamics in sweet cherry, <i>Prunus avium</i> (Rosaceae)
Bibliographic record
Abstract
Prevailing ambient temperature during the reproductive phase is one of several important factors for seed and fruit set in different plant species, and its consequences on reproductive success may increase with global warming. The effect of temperature on pollen performance was evaluated in sweet cherry (Prunus avium L.), comparing as pollen donors two cultivars that differ in their adaptation to temperature. 'Sunburst' is a cultivar that originated in Canada with a pedigree of cultivars from Northern Europe, while 'Cristobalina' is a cultivar native to southeast Spain, adapted to warmer conditions. Temperature effects were tested either in controlled-temperature chambers or in the field in a plastic cage. In both genotypes, an increase in temperature reduced pollen germination, but accelerated pollen tube growth. However, a different genotypic response, which reflected the overall adaptation of the pollen donor, was obtained for pollen tube dynamics, expressed as the census of the microgametophyte population that successfully reached the base of the style. While both cultivars performed similarly at 20°C, the microgametophyte population was reduced at 30°C for Sunburst and at 10°C for Cristobalina. These results indicate a differential genotypic response to temperature during the reproductive phase, which could be important in terms of the time needed for a plant species to adapt to rapid temperature changes.
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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) | 0.000 | 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 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".