Microbial diversity and capacity for arsenic biogeochemical cycling in aquifers associated with thermal mobilization
Bibliographic record
Abstract
Thermal recovery technologies for in-situ bitumen extraction can result in the heating of surrounding aquifers, potentially mobilizing arsenic naturally present in the sediments to the groundwater. The relative toxicity of dissolved arsenic is related to its speciation, with As(V) being less toxic than As(III). Microorganisms have various mechanisms of arsenic detoxification and metabolism, which include genes for efflux, methylation, and reduction/oxidation of As(V)/As(III). We characterized the microbial communities along two aquifer transects associated with thermally mobilized arsenic near Northeastern Alberta oil sands deposits. 16S rRNA amplicons and metagenomic sequencing data of biomass from filtered groundwater indicated major changes in the dominant taxa between wells, especially those currently experiencing elevated arsenic concentrations. Annotation of arsenic-related genes indicated that efflux pumps (arsB, acr3), intracellular reduction (arsC) and methylation (arsM) genes were widespread among community members but comparatively few organisms encoded genes for arsenic respiratory reductases (arrA) and oxidases (arxA, aioA). While this indicates that microbes have the capacity to exacerbate arsenic toxicity by increasing the relative concentration of As(III), some populations of iron oxidizing and sulfate reducing bacteria (including novel Gallionella and Thermodesulfovibrionia populations) show potential for indirect bioremediation through formation of insoluble iron/sulfide minerals which adsorb or coprecipitate arsenic. An unusually high proportional abundance of a single Paceibacteria population that lacked arsenic resistance genes was identified in one high‑arsenic well, and we discuss hypotheses for its ability to persist. Overall, this study describes how aquifer microbial communities respond to thermal and arsenic plumes, and predicts potential contributions of microbes to arsenic biogeochemical cycling under this disturbance.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".