Assessment of Freezing Injury in Palm Species by Chlorophyll Fluorescence
Bibliographic record
Abstract
Freezing temperatures present major constraints for palm cultivation in temperate regions. As a result of their landscape value, there is a constant need for appropriate species and cultivars for freeze-prone areas. The objective of the present study was to evaluate the suitability of the chlorophyll fluorescence technique for quantitative assessment of freezing injury in palms. Five palm species known to differ in their freezing tolerance were selected: Copernicia alba , Washingtonia filifera , Sabal palmetto , Trachycarpus fortunei , and Rhapidophyllum hystrix . Leaf segments were frozen at –5, –10, –15, and –20 °C for 1 h. Repeated freezing–thawing cycles were additionally performed in young and older leaves of R. hystrix . Depending on the species and temperature, significant differences in the ratio of variable-to-maximal fluorescence (F v /F m ) were detected 3 h after the freezing treatment, whereas visual symptoms appeared after 24 h. A strong positive correlation ( r 2 = 0.94) was found between the injury index calculated from F v /F m values and the index of injury based on the electrolyte leakage technique. Although both indices provided similar information, the nondestructive chlorophyll fluorescence method allows monitoring the progression of damage as well as the eventual recovery taking place in the leaf tissue after freezing.
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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".