Biotransformation of selenium and arsenic in multi-species biofilm
Bibliographic record
Abstract
Environmental context Elevated levels of selenium and arsenic in the environment as a result of anthropogenic activities are creating significant concerns for the health of aquatic ecosystems. How biofilms, or aquatic microbial communities, interact with and chemically modify selenium and arsenic species has been examined. The results demonstrate that selenium and arsenic induce structural changes in biofilms, and concurrently undergo extensive biotransformation, in most cases to less bioavailable species. Abstract Arsenic and selenium are both elements of concern especially when released into the environment by anthropogenic activity. Biofilms, or communities of microorganisms, can play important roles in biotransforming elements to less toxic chemical forms. This study used novel tools to characterise the fate of oxyanions (selenate, selenite, arsenate or arsenite) in multi-species biofilms inoculated from a source receiving coal mining effluent. Confocal laser scanning microscopy (CLSM) demonstrated a distinct biofilm morphology at elevated oxyanion concentrations. Selenium and arsenic K near-edge X-ray absorption spectroscopy (XAS) showed biofilm biotransformation of oxyanions; extended X-ray absorption fine structure (EXAFS) confirmed elemental selenium as a product. Micro X-ray fluorescence imaging combined with CLSM revealed highly localised reduced selenium species in the biofilm. Isolation and partial 16S rRNA gene sequencing suggested four principle bacterial genera were responsible. Biofilms can both detoxify and sequester selenium and arsenic, playing critical roles in their fate and effects in aquatic 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) | 0.000 | 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 | 0.001 |
| 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".