Effect of Cold Plasma Seed Treatment on Growth and Nitrogen Fixation Traits in Field Pea (Pisum sativum L.) and Soybean (Glycine max L.) Under Cold Stress
Bibliographic record
Abstract
Cold stress during early growth can severely impact nodulation, growth, and yield in legumes. This study evaluated cold plasma (CP) seed treatment as a strategy to enhance growth and symbiotic nitrogen fixation (SNF) in field pea (Pisum sativum L.) and soybean (Glycine max L.) under cold stress during early growth. CP-treated and non-treated seeds were grown at 8 °C (cold) or 15 °C (control) for 5 weeks, after which half of the plants were harvested for nodulation and growth assessments. The remainder were transferred to greenhouse conditions until maturity. The cold stress suppressed nodulation and reduced biomass in both legumes. Soybean recovered under greenhouse conditions; however, pea yield remained suppressed. At maturity, SNF traits in both legumes were not significantly affected by early cold stress. CP seed treatment showed little effect under severe cold (8 °C) but at 15 °C, improved root growth in pea and enhanced root and shoot biomass and pod and seed yield in soybean. These findings suggest that CP seed treatment can improve legume performance under moderate cold. However, the current CP seed treatment conditions did not improve the stress resistance of both crops under severe cold stress.
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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".