Effect of phenol on germination capacity and polyphenol oxidase, peroxidase and catalase activities in lettuce
Bibliographic record
Abstract
In this study we examined the activities of polyphenol oxidase (PPO) and antioxidant enzymes, peroxidase (POX) and catalase (CAT) during lettuce seed germination at different concentrations of phenol. Out of eleven varieties of lettuce, four were chosen according to their germination tolerance to phenol as follows: plants exhibiting high (Ljubljanska ledenka - LJL and Nansen - N) and low toleranace (Little Gem - LG and Majska kraljica - MK). A decrease in germination efficiency after exposure to LD50 of phenol was determined for these four varieties. The effects of phenol treatment on POX, CAT and PPO activities were determined after 4, 5, 6, 7 and 8 days of growth at LD50 concentrations. A trend of increased peroxidase activity was observed in seeds grown on LD50 of phenol compared to control seeds. A significant increase in CAT activity was observed at the beginning of treatment for MK, LG and N in seeds grown on phenol as well as in control seeds. A trend of increased PPO activity was observed in all control seeds. We also investigated the affinity of PPO for two different substrates that were used for the determination of enzyme activity. Our results show that LJL and N are the varieties most tolerant to growth on phenol. Here we report on the activities of their antioxidant enzymes and PPO during seed germination.
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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 | 0.001 |
| 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".