Differential Impacts of <scp>pH</scp> and Acid Type on Seed Germination and Seedling Growth in <i>Brassica juncea</i> and <i>Raphanus sativus</i>
Bibliographic record
Abstract
Elevated acidity from natural and anthropogenic sources can be a significant stressor for plants, affecting essential processes such as nutrient uptake and growth. While low pH (< 4) is generally considered stressful for plants, differential impacts of distinct acid types-organic versus inorganic, strong versus weak-on plant growth and development remain unclear. To address this knowledge gap, we evaluated the responses of two Brassicaceae species to organic (acetic) and inorganic (hydrochloric, sulfuric) acids at three pH levels (pH 2.5-5.5) to determine whether acid responses are solely pH-dependent or if acid type specificity plays a role in plant functional responses, thereby providing insights into the mechanisms of acid responses and tolerance. Plant responses varied based on acid types, even at the same pH levels. Hydrochloric acid promoted higher seed germination but hindered seedling growth and morphological development of roots, shoots, and leaves, while acetic and sulfuric acids had the opposite effects. These results highlight the influence of acid types on plant functions, specifically affecting distinct developmental stages. These findings suggest that plant responses to acid stress are not solely pH-dependent but are significantly influenced by the specific chemical properties of the acids involved, providing valuable insights for managing acid-impacted wild plant communities.
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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".