In vitro rhizobia response and symbiosis process under aluminum stress
Bibliographic record
Abstract
Aluminum (Al) toxicity is a major problem affecting soil fertility, microbial diversity, and nutrient uptake of plants. Rhizobia response and legume interaction under Al conditions are still unknown; it is important to understand how to develop and improve legume cultivation under Al stress. In this study, rhizobia response was recorded under different Al concentrations. Al effect on rhizobial cells was characterized by combination with different two pH conditions. Symbiosis process was compared between α- and β-rhizobia inoculated onto soybean varieties. Rhizobial cell numbers was decreased as Al concentration increased. However, induced Al tolerance considerably depended on rhizobia types and their origins. Accordingly, organic acid results were in correlation with growth rate and cell density which suggested that citric acid might be a positive selective force for Al tolerance and plant interaction on rhizobia. Al toxicity delayed and interrupted the plant-rhizobia interaction and the effect was more pronounced under acidic conditions. Burkholderia fungorum VTr35 significantly improved plant growth under acid-Al stress in combination with all soybean varieties. Moreover, plant genotype was an important factor to establish an effective nodulation and nitrogen fixation under Al stress. Additionally, tolerant rhizobia could be applied as an inoculant on stressful agroecosystems. Furthermore, metabolic pathways have still been unknown under Al 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".