Short-Term Liquid Nitrogen Storage of Maize, Common Bean and Soybean Seeds Modifies Their Biochemical Composition
Bibliographic record
Abstract
We studied the effects of liquid nitrogen storage of maize, common bean and soybean seeds on their germination, electrolyte leakage, levels of chlorophylls, phenolics, aldehydes, proteins and peroxidase activity. After storage for 28 days, seeds were retrieved from liquid nitrogen, some were set to germinate and others were analyzed biochemically. No phenotypic modifications were observed visually 5 days after beginning of germination, although percentage of seed germination was reduced by LN in maize and soybean. Moreover, numerous significant effects of seed cryopreservation were recorded at the biochemical status. In maize seeds, the most important and statistically significant modifications were observed in the increased levels of chlorophyll b and total chlorophyll pigments and in the decreased contents of free phenolics after 28 days of exposure to LN, compared to the control treatment. In common bean, relevant changes were observed in the increased electrolyte leakage and in the reduced levels of chlorophyll pigments (b, total) and free phenolics. In soybean, modifications were observed in the increased levels of chlorophyll pigments (a, b, total), malondialdehyde and electrolyte leakage, and in the decreased peroxidase activity. We have shown for the first time that immersion of maize, common bean and soybean seeds in liquid nitrogen modified the levels of different biochemicals.
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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".