Assessment of intra-species diversity among strains of<i>Acinetobacter</i><i>baumannii</i>isolated from sites contaminated with petroleum hydrocarbons
Bibliographic record
Abstract
A total of 96 crude oil-degrading bacterial strains were isolated from 5 geographically diverse sites in India that were contaminated with different types of petroleum hydrocarbons. The strains were identified by sequencing the genes that encode for 16S rRNA. Out of the 96 isolates, 25 strains were identified as Acinetobacter baumannii and selected for the study. All of the selected strains could degrade the total petroleum hydrocarbon fractions of crude oil. These 25 strains were biochemically profiled and grouped into 8 phenovars on the basis of multivariate analysis of their substrate utilization profiles. PCR-based DNA fingerprinting was performed using intergenic repetitive DNA sequences, which divided the selected 25 strains into 7 specific genomic clusters. tRNA intergenic spacer length polymorphism was performed to determine the intra-species relatedness among these 25 strains. It delineated the strains into 8 genomic groups. The present study detected specific variants among the A. baumannii strains with differential degradation capacities for different fractions of crude oil. This could play a significant role in in situ bioremediation. The study also revealed the impact of environmental factors that cause intra-species diversity within the selected strains of A. baumannii.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".