Genetic and phenotypic traits of streptomycetes used to characterize antibiotic activities of field-collected microbes
Bibliographic record
Abstract
Although antibiotic production may contribute significantly to microbial fitness, there is limited information on the ecology of antibiotic-producing microbial populations in soil. Indeed, quantitative information on the variation in frequency and intensity of specific antibiotic inhibitory and resistance abilities within soil microbial communities is lacking. Among the streptomycetes, antibiotic production is highly variable and resistance to antibiotics is highly specific to individual microbial strains. The objective of this work was to genetically and phenotypically characterize a reference collection of streptomycetes for use in distinguishing inhibition and resistance phenotypes of field-collected microbes. Specifically, we examined inhibition and resistance abilities of all isolates in all possible pairwise combinations, genetic relatedness using BOX-PCR and 16S rDNA sequence analyses, nutrient utilization profiles, and antibiotic induction among all possible three-way combinations of isolates. Each streptomycete isolate possessed a unique set of phenotypic and genetic characteristics. However, there was little correspondence between phenotypic and genetic traits. This collection of reference isolates provides the potential for distinguishing 1024 inhibition and resistance phenotypes in field-collected microbes. Relationships between the genetic and phenotypic characteristics examined may provide preliminary insight into the distinct strategies that microbes use in optimizing their fitness in natural environments.
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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 | 0.001 | 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 | 0.000 | 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".