Utilizing a Metagenome Assembled Genome Approach Revealed Further Insights into Microbially Mediated Heavy-Metal Resistance in Soils from a Former Nuclear Materials Production Facility
Bibliographic record
Abstract
Soils and sediments from the Savannah River Site (SRS), located in the USA are known to have a long history of co-contamination with radionuclides (mainly uranium) and heavy metals. To better understand the bacterial taxonomic and genomic characteristic of the SRS soil habitat, shotgun metagenomes were obtained from three different levels of contaminated soil—high, medium, and low. Sequences were then assembled and annotated to generate metagenome-assembled genomes (MAGs) using toolkits within the nf-core/mag. The initial analysis resulted in a total of 254 MAGs. After bin refinement and de-replication, 55 MAGs which met the quality standard with a completeness > 75% and contamination < 25%, accounting for 21.67% of all the MAGs, were reconstructed. Further refinement with completeness > 90% and contamination < 10% yielded 24 MAGs (18 from the winter season and 6 from the summer season) spanning 6 bacterial phyla, predominantly Actinomycetota, Proteobacteriota, Bacteroidota, and Cyanobacteria. Overall, the Arthrobacter MAG was found to be robust for further analysis, with over 1749 genes putatively involved in the crucial metabolism of elements viz. nitrogen, phosphorous, and sulfur, and 598 genes encoding enzymes for the resistance of metals including cadmium, zinc, chromium, arsenic, and copper. In summary, this project enhances our understanding of genes conferring resistance to heavy metals in uranium-contaminated soils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".