Profiles of wheat rhizobacterial communities in response to repeated glyphosate applications, crop rotation, and tillage
Bibliographic record
Abstract
Due to widespread adoption of no-till management and use of glyphosate-resistant transgenic crops, glyphosate is the most widely used herbicide worldwide. However, its effect on soil microbial communities is inconsistent. We studied the effects of glyphosate, tillage, and crop rotation on the diversity and composition of soil bacterial communities in wheat (Triticum turgidum var. durum Desf.) rhizosphere after 6 and 7 yr of glyphosate applications. In a 2 × 2 × 2 factorial design, there were two crop rotation treatments: continuous wheat (W–W) and wheat in rotation with field pea (Pisum sativum L.) (P–W); two tillage treatments: minimum tillage (MT) and no-till (NT); and two glyphosate treatments: no application or pre-seeding application at the recommended rate. None of the treatments affected wheat rhizobacterial α-diversity or the relative abundances of most bacterial groups. The most abundant phyla were Proteobacteria (25.1% relative abundance), Actinobacteria (21.7%), Acidobacteria (8.7%), Bacteroidetes (5.9%), Firmicutes (1.4%), Armatimonadetes (1.3%), and Verrucomicrobia (1.2%). Glyphosate reduced the relative abundance of Alphaproteobacteria in W–W rotation but increased it in P–W rotation, and it reduced the relative abundance of Opitutus spp. The W–W rotation had greater relative abundances of the classes Bacilli (Firmicutes) and Gammaproteobacteria, and genera Bacillus and Opitutus (Verrucomicrobia), than the P–W rotation. Compared with MT, NT increased the relative abundance of the phylum candidate division WPS-1, but it reduced that of Phenylobacterium spp. in W–W rotation. These treatment effects probably had implications for soil functioning, including nutrient cycling and biological disease/pest control.
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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".