Phenotypic and genetic analysis of Enterobacter spp. from a Brazilian oligotrophic freshwater lake
Bibliographic record
Abstract
We characterized a population of Enterobacter spp. of the Enterobacter cloacae complex isolated from an oligotrophic lake; most isolates were identified as E. cloacae. Fingerprinting polymerase chain reaction (PCR), along with morphological, biochemical, physiological, and plasmid profiles analyses, including antimicrobial susceptibility testing, were performed on 22 environmental isolates. Misidentification occurred when using the API 20E identification system. Analysis of 16S rDNA sequences confirmed the close relatedness between species of the E. cloacae complex. The tDNA PCR allowed the differentiation and identification of the E. cloacae isolates. Evaluation of genetic diversity by 16S rDNA sequence, tDNA, internal transcribed spacers, and enterobacterial repetitive intergenic concensus profiles revealed nearly identical isolates, although they exhibited different physiological and antimicrobial resistance profiles. Among the Enterobacter isolates, 96% were resistant to at least one antimicrobial; multiple resistance was also found at a high frequency (86%). The antimicrobials against which resistance was found most frequently were beta-lactams, chloramphenicol, and streptomycin. Plasmids were found in 21 of the 22 Enterobacter isolates. This confirms the conception that antibiotic resistance can occur in oligotrophic freshwater lake bacteria, which has important implications for public health.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".