Temporal and spatial interactions modulate the soybean microbiome
Bibliographic record
Abstract
Managed agricultural ecosystems are unique systems where crops and microbes are intrinsically linked. This study focuses on discerning microbiome successional patterns across all plant organs and tests for evidence of niche differentiation along temporal and spatial axes. Soybean plants were grown in an environmental chamber till seed maturation. Samples from various developmental stages (emergence, growth, flowering and maturation) and compartments (leaf, stem, root and rhizosphere) were collected. Community structure and composition were assessed with 16S rRNA gene and ITS region amplicon sequencing. Overall, the interaction between spatial and temporal dynamics modulated alpha and beta diversity patterns. Time lag analysis on measured diversity indices highlighted a strong temporal dependence of communities. Spatial and temporal interactions influenced the relative abundance of the most abundant genera, whilst random forest predictions reinforced the observed localisation patterns of abundant genera. Overall, our results show that spatial and temporal interactions tend to maintain high levels of biodiversity within the bacterial/archaeal community, whilst in fungal communities OTUs within the same genus tend to have overlapping niches.
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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".