Soil–environment interaction with arbuscular mycorrhizal fungi in three cold desert plants of Himalaya, India
Bibliographic record
Abstract
Arbuscular mycorrhizal fungi (AMFs) are well known to establish symbiotic associations with plant roots, and assist to enhance nutrient uptake and contribute to improve soil structure and fertility. This study investigated the AMF diversity and physiochemical properties of rhizosphere soil associated with three ethno-medicinal plants— Inula racemosa, Saussurea costus (cultivated), and Hyoscyamus niger (wild) from cold desert areas of Himachal Pradesh, India. Results showed that soil samples were moderately alkaline (pH 7.17–8.23), with low to medium levels of organic carbon, nitrogen, phosphate, and potassium. Cultivated soils showed higher macronutrient levels than noncultivated lands. Principal component analysis indicated a positive correlation between root colonization and pH, organic carbon, and phosphorus, while a negative correlation was found with electric conductivity, nitrogen, and potassium. Root colonization varied across medicinal plants, with Glomus and Acaulospora being the dominant AMF genera. A total of 14 arbuscular mycorrhizal fungal species from 6 genera were identified. The study indicates a potentially rich diversity of AMFs in cold desert ecosystems, particularly within the Glomeraceae family. Utilizing these AMFs in off site cultivation and conservation efforts, through single spore cultures or AMF consortia, could be beneficial for promoting sustainable growth of these valuable plants.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".