Cultivation of microorganisms from sulfidic mine waste and genomic insights into acidibacillus ferrooxidans and penicillium sp
Bibliographic record
Abstract
With increasing pressure to support sustainable mining initiatives, the advancement of biotechnologies is essential for dealing with mine waste in Canada and around the world. These improvements require a thorough understanding of microorganisms that inhabit different waste materials. This study used classic culturing techniques to isolate acidophilic ironoxidizing bacteria and genomic sequencing to characterize microbial isolates from different source materials. Two organisms were isolated from an enrichment culture that was inoculated with low-sulfur waste rock from a Canadian mine site. They were identified as a strain of Acidibacillus ferrooxidans and a fungus of the genus Penicillium. Some of the genes that were annotated from the sequenced prokaryotic genome were absent in available genomes of Ab. ferrooxidans and others were associated with metabolic abilities that have not been described in this organism, such as respiratory nitrate reduction. A total of 59 stress response genes were identified including for resistance to several heavy metals and multiple antibiotics. Fungal biomass displayed iron oxidation as well as accumulation, and 4 genes encoding for multicopper oxidase enzymes were annotated which have been associated with the adaptation of fungi in metal-rich environments. This study also tailored selective media with the aim of isolating bacteria from the genera Leptospirillum and Sulfobacillus from sulfidic bioreactor cultures. These groups were not identified but we started to isolate at least 2 different colonies of interest. This project provides insight into microorganisms from these waste materials and their potential in biotechnologies. It also emphasizes the importance of assessing fungi in these 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".