Bacteria Isolated from Canada’s White Rabbit Cave Revealed Antimicrobial Activities
Bibliographic record
Abstract
Caves offer a unique habitat for microorganisms, which allow the adaptation of exclusive metabolic pathways to the resources available. This environment could enable the production of primary and secondary metabolites with unique antimicrobial or enzymatic properties. White Rabbit Cave is located in the Monashee Mountain range in south-central British Columbia, a metamorphic range not known for cave and karst development. The present study has recovered bacterial isolates from the White Rabbit Cave and assessed them for their antimicrobial properties by employing the agar plug assay. One hundred and six bacterial isolates were cultivated from the collected samples, among which, five bacterial isolates displayed antimicrobial properties against a methicillin-resistant Staphylococcus aureus (MRSA) strain. Furthermore, these five isolates were identified with 16S rRNA gene sequencing and through phylogenetic analysis. It has been observed that three of them (B076, B053, and B079) were identified as Streptomyces spp. (Phylum Actinobacteria) while the other two were recognized as Paenibacillus spp. (B039) and Paenibacillus terrae (B016) (Phylum Firmicutes). To the best of our knowledge, this is the first study that identifies bacterial species with antimicrobial properties from White Rabbit Cave in the Monashee Mountain range in British Columbia, Canada.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".