Comparative phylogenetic analysis of microbial communities in pristine and hydrocarbon-contaminated Alpine soils
Bibliographic record
Abstract
A molecular characterization of pristine and petroleum hydrocarbon-contaminated Alpine soils sampled in Tyrol (Austria) was performed. To identify predominant bacteria, PCR-amplified 16S rRNA gene fragments from five pristine and nine contaminated soils were analysed using denaturing gradient gel electrophoresis (DGGE). Sequencing and phylogenetic analyses demonstrated that the majority of the DGGE bands represented bacteria in the Actinobacteria and Proteobacteria phyla: 18 and 73%, respectively, in pristine soils, compared with 20 and 76%, respectively, in contaminated soils. A different distribution pattern of bacterial classes in the Proteobacteria was observed between pristine and contaminated soils. The relative proportion of microorganisms belonging to the Alphaproteobacteria was larger in pristine (46%) than in contaminated (24%) soils, while Betaproteobacteria and Gammaproteobacteria were detected only in the hydrocarbon-contaminated soils. This result compared favourably with earlier work in which hydrocarbon-degradation genotypes, largely pseudomonads and Acinetobacter, belonging to the Gammaproteobacteria, were enriched following oil hydrocarbon contamination. In contrast, members of the Actinobacteria phylum, represented by Rhodococcus and Mycobacterium, were found in pristine soils where contamination events had not occurred. The results demonstrate a significant shift in the microbial community structure in Alpine soils following contamination. Furthermore, more potentially novel phylotypes were found in the pristine soils than in the contaminated soils.
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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 | 0.001 |
| 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".