Variability in colonization of arbuscular mycorrhizal fungi and its effect on mycorrhizal dependency of improved and unimproved soybean cultivars
Bibliographic record
Abstract
Breeding selection of germplasm under fertilized conditions may reduce the frequency of genes that promote mycorrhizal associations. This study was developed to compare variability in mycorrhizal colonization and its effect on mycorrhizal dependency (MD) in improved soybean genotypes (I-1 and I-2) with differential tolerance to drought stress, and in unimproved soybean genotypes (UI-3 and UI-4). As inoculum, a mixed native arbuscular mycorrhizal fungi (AMF) was isolated from soybean roots, showing spores mostly of the species Funneliformis mosseae. At 20 days, unimproved genotypes followed by I-2, showed an increase in arbuscule formation, but not in I-1. At 40 days, mycorrhizal plants showed an increase in nodulation, this effect being more evident in unimproved genotypes. Mycorrhizal dependency, evaluated as growth and biochemical parameters from oxidative stress was increased in unimproved and I-2 since 20 days, whereas in I-1, MD increased at 40 days. We cannot distinguish significant differences in AMF colonization and MD between unimproved and I-2. However, variability among improved genotypes was observed. Our results suggest that selection for improved soybean genotypes with good and rapid AMF colonization, particularly high arbuscule/hyphae ratio could be a useful strategy for the development of genotypes that optimize AMF contribution to cropping systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".