Invasion by Conyza sumatrensis alters soil microbial community structure in urban ecosystems
Bibliographic record
Abstract
Whether invasive plants stimulate or inhibit the soil microbial diversity is still an open question. Despite large-scale invasion by Conyza sumatrensis (Retz.) E. Walker in the urban ecosystems of the Srinagar city of the Kashmir Himalayan region, limited information exists on its impact, particularly, on the belowground microbial diversity. The present study was thus conducted to compare the soil microbial (bacterial and ascomycetous fungal) diversity between the sites invaded by C. sumatrensis and un-invaded (control) sites. Soil metagenome was extracted from C. sumatrensis invaded and un-invaded plots at the three study sites. A total of six plots (5 × 5 m each in size), including three invaded by C. sumatrensis and three un-invaded plots were nested within each study site. DNA after amplification was subject to denaturing gradient gel electrophoresis (DGGE); the bands were extracted from the DGGE gel, re-amplified, and sequenced for identification of the species. The number of bacterial species was reduced in the invaded plots at two out of the three sites while as it was relatively higher in the un-invaded plots with many species exclusively found in these plots. Fungal species richness was higher in the invaded plots compared to the un-invaded plots at all the three sites. Also, more fungal species were found to occur exclusively in the invaded plots without being represented in the un-invaded plots. Invasion by C. sumatrensis alters soil microbial community structure in the urban ecosystems in the Kashmir Himalaya. How this species does so and what benefits does it draw from such alteration promise to be an interesting future discourse.
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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".