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 \nadvancement of biotechnologies is essential for dealing with mine waste in Canada and around \nthe world. These improvements require a thorough understanding of microorganisms that inhabit \ndifferent waste materials. This study used classic culturing techniques to isolate acidophilic ironoxidizing bacteria and genomic sequencing to characterize microbial isolates from different \nsource materials. Two organisms were isolated from an enrichment culture that was inoculated \nwith low-sulfur waste rock from a Canadian mine site. They were identified as a strain of \nAcidibacillus ferrooxidans and a fungus of the genus Penicillium. Some of the genes that were \nannotated from the sequenced prokaryotic genome were absent in available genomes of Ab. \nferrooxidans and others were associated with metabolic abilities that have not been described in \nthis organism, such as respiratory nitrate reduction. A total of 59 stress response genes were \nidentified including for resistance to several heavy metals and multiple antibiotics. Fungal \nbiomass displayed iron oxidation as well as accumulation, and 4 genes encoding for multicopper \noxidase enzymes were annotated which have been associated with the adaptation of fungi in \nmetal-rich environments. This study also tailored selective media with the aim of isolating \nbacteria from the genera Leptospirillum and Sulfobacillus from sulfidic bioreactor cultures. \nThese groups were not identified but we started to isolate at least 2 different colonies of interest. \nThis project provides insight into microorganisms from these waste materials and their potential \nin biotechnologies. It also emphasizes the importance of assessing fungi in these environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".