Comparative analysis of the soil microbiome and carbohydrate content of Anthoxanthum nitens (Sweetgrass) and other Poaceae grass tissues and associated soils
Bibliographic record
Abstract
Sweetgrass (Anthoxanthum nitens) is a culturally and environmentally significant perennial grass to many Indigenous Peoples; however, little is known about the potential of Sweetgrass as a contributor to soil health, biodiversity, and climate adaptation. Here, a team of transdisciplinary experts from academia, a non-governmental organization, and a First Nation community collaborated to investigate the structural composition of the rhizomes, stems, and leaves of greenhouse-grown Sweetgrass in comparison to other Poaceae grass members found in a nearby field. The data shows that the monosaccharide composition of A. nitens was evenly distributed throughout the three tissues, and that cellulose was the predominant polysaccharide followed by glucuronoararbinoxylans. There were lesser amounts of xyloglucans, mixed-linkage glucans, homogalacturonans, and rhamnogalacturonans as the hemicellulosic and pectic polysaccharides, respectively. The carbohydrate composition seen in A. nitens was consistent with the other Poaceae grasses evaluated in this study, with the exception of Setaria chondrachne, which contained elevated pectin levels in its stems and leaves. Additionally, the analysis of the carbohydrate content within the soil samples revealed a higher abundance of carbohydrates within greenhouse soil when compared to field soil samples, with significantly more mannose, galactose, and galacturonic acid. Further, there were structural differences in the microbial communities across sampling sites, including a significant increase in the abundance of Bacillus spp. in the greenhouse soil. Overall, this study provides the glycome and associated soil microbial community baseline for greenhouse-grown Sweetgrass.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".