Inoculation with Rhizophagus irregularis Does Not Alter Arbuscular Mycorrhizal Fungal Community Structure within the Roots of Corn, Wheat, and Soybean Crops
Bibliographic record
Abstract
Little is known about establishment success of the arbuscular mycorrhizal fungal (AMF) inocula and their effects on a soil-indigenous community of AMF. In this study, we assessed the effect of introducing Rhizophagus irregularis DAOM-197198 in soil under field condition on the community composition of indigenous AMF in the roots of corn (Zea mays), soybean (Glycine max), and wheat (Triticum aestivum). Three field trials were conducted with inoculated and non-inoculated plots. Four to ten roots and their rhizosphere soil samples of two growth stages for corn and wheat, and one growing stage of soybean, were collected, totalling 122 root and soil samples. Root colonization was measured microscopically, and the fungal communities were determined by paired-end Illumina MiSeq amplicon sequencing using 18S rDNA marker. After quality trimming and merging of paired ends, 6.7 million sequences could be assigned to 414 different operational taxonomic units. These could be assigned to 68 virtual taxa (VT) using the AMF reference sequence database MaarjAM. The most abundant VT corresponded to R. irregularis. The inoculation treatment did not influence the presence of R. irregularis, or AMF community diversity in roots. This seems to indicate that inoculation with R. irregularis DAOM-197198 does not change the indigenous AMF community composition, probably because it is already present in high abundance naturally.
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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) | 0.000 | 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".