A differential capacity of arbuscular mycorrhizal fungal colonization under well-watered conditions and its relationship with drought stress mitigation in unimproved vs. improved soybean genotypes
Bibliographic record
Abstract
Modern breeding programs may cause a reduction in plant responsiveness to arbuscular mycorrhizal (AM) fungi. In this study, we tested the hypothesis that responses such as higher arbuscule formation and mycorrhizal dependency (MD) in unimproved soybean genotypes than in improved genotypes is related to drought stress tolerance caused by enhanced growth parameters and oxidative stress regulation. Firstly, four unimproved and four improved soybean genotypes were compared under well-watered conditions. After 20 days, all of the unimproved soybean genotypes showed increased arbuscule formation, as well as a positive and higher MD index in foliar mineral nutrient and growth parameters compared with the four improved genotypes. Secondly, tolerance to drought stress was evaluated in the two improved soybean genotypes and the two unimproved genotypes selected for the most contrasting response to arbuscule formation under well-watered conditions. After 20 days of 30% of field capacity, arbuscule formation was higher in the unimproved than improved genotypes. Mycorrhizal dependency evaluated as leaf area as well as shoot and root dry mass were highest in the unimproved AM genotypes. Moreover, levels of malondiadehide were lower and proline was higher in the unimproved rather than the improved genotypes. The potential capacity of arbuscule formation is discussed as a selection criterion to identify improved soybean genotypes with increased efficiency under well-watered conditions and an enhanced capacity to relieve drought 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".