Microbial Diversity in Engineered Haloalkaline Environments Shaped by Shared Geochemical Drivers Observed in Natural Analogues
Bibliographic record
Abstract
Microbial communities in engineered terrestrial haloalkaline environments have been poorly characterized relative to their natural counterparts and are geologically recent in formation, offering opportunities to explore microbial diversity and assembly in dynamic, geochemically comparable contexts. In this study, the microbial community structure and geochemical characteristics of three geographically dispersed bauxite residue environments along a remediation gradient were assessed and subsequently compared with other engineered and natural haloalkaline systems. In bauxite residues, bacterial communities were similar at the phylum level (dominated by Proteobacteria and Firmicutes) to those found in soda lakes, oil sands tailings, and nuclear wastes; however, they differed at lower taxonomic levels, with only 23% of operational taxonomic units (OTUs) shared with other haloalkaline environments. Although being less diverse than natural analogues, bauxite residue harbored substantial novel bacterial taxa, with 90% of OTUs nonmatchable to cultured representative sequences. Fungal communities were dominated by Ascomycota and Basidiomycota, consistent with previous studies of hypersaline environments, and also harbored substantial novel (73% of OTUs) taxa. In bauxite residues, community structure was clearly linked to geochemical and physical environmental parameters, with 84% of variation in bacterial and 73% of variation in fungal community structures explained by environmental parameters. The major driver of bacterial community structure (salinity) was consistent across natural and engineered environments; however, drivers differed for fungal community structure between natural (pH) and engineered (total alkalinity) environments. This study demonstrates that both engineered and natural terrestrial haloalkaline environments host substantial repositories of microbial diversity, which are strongly shaped by geochemical drivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".