Physiological Effects of Hydration-dehydration on Enhancing Heat Tolerance of Tomato Seeds during Germination
Bibliographic record
Abstract
Tomato is originated form western plateau of South America.It is seasoned with highland dry and cool climate of its native habitat around equator,intolerant of high temperature and humidity,and more sensitive to high temperature especially during its germination period.The previous research shows that temperature over 30 ℃ severely inhibit the germination of tomato seeds.In the present study,germination percentage,biomass,vigor index,lipid peroxidation and antioxidant enzyme activities of Lycopersicon esculentum Mil 1.CV Jiafen No.17 seeds under high temperature were detected to study the effects of hydration-dehydration on raising its heat tolerance in germinating stage.After being hydrated for 30 h at 25 ℃,tomato seeds were dried under room temperature,and then used for germination experiment at 33 ℃ and 35 ℃ respectively.Antioxidant enzyme activities,relative electricity conductivity(REC) and malondialdehyde(MDA) content were detected 30 h after the seeds were set on paper bed,while germination percentage,biomass and vigor index were detected 10 d later.Results showed that after hydration-dehydration treatment,germination percentage,biomass,vigor index were all increased,superoxide dismutase(SOD),ascorbate peroxidase(APX),catalase(CAT),glutathione reductase(GR) activities were also significantly increased at different degrees,while REC and MDA content were decreased.In conclusion,it is possible that hydration-dehydration treatment increased tomato seeds' heat tolerance by repairing the injured membrane system,enhancing the antioxidase activities and reducing the leakage and lipid peroxidation.
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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".