Deciduous and evergreen perennials have higher antioxidant levels and more reactive oxygen species-susceptible chlorophyll-binding proteins
Bibliographic record
Abstract
Abstract Perennials live longer than annuals, yet the underlying physiological mechanisms responsible are poorly understood. We gathered data from published reports to investigate two hypotheses based on the oxidative stress theory of ageing. The first hypothesis was that perennials maintain higher antioxidant levels to neutralize reactive oxygen species (ROS) better, before they cause oxidative damage. Although carotenoid levels did not differ between annuals and perennials, we found that deciduous perennials had higher activity of the ascorbate–glutathione cycle, and evergreen perennials had higher activity of superoxide dismutase (SOD, EC 1.15.1.1). The second hypothesis was that chlorophyll-binding proteins of perennials have a lower proportion of ROS-susceptible amino acids to protect chlorophyll better from oxidative damage. Contrary to our predictions, although LHCI, LHCII, CP26, and CP29 showed no difference in amino acid composition between annuals and perennials, D1 protein and CP24 had a higher proportion of ROS-susceptible amino acids in both deciduous and evergreen perennials. By being more susceptible to ROS attack, these proteins might minimize oxidative damage to chlorophyll and/or contain oxidative damage within the photosystems such that it does not spread to other cell regions.
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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 | 0.001 | 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".