Influence of geogenic factors on microbial communities in metallogenic Australian soils
Bibliographic record
Abstract
Links between microbial community assemblages and geogenic factors were assessed in 187 soil samples collected from four metal-rich provinces across Australia. Field-fresh soils and soils incubated with soluble Au(III) complexes were analysed using three-domain multiplex-terminal restriction fragment length polymorphism, and phylogenetic (PhyloChip) and functional (GeoChip) microarrays. Geogenic factors of soils were determined using lithological-, geomorphological- and soil-mapping combined with analyses of 51 geochemical parameters. Microbial communities differed significantly between landforms, soil horizons, lithologies and also with the occurrence of underlying Au deposits. The strongest responses to these factors, and to amendment with soluble Au(III) complexes, was observed in bacterial communities. PhyloChip analyses revealed a greater abundance and diversity of Alphaproteobacteria (especially Sphingomonas spp.), and Firmicutes (Bacillus spp.) in Au-containing and Au(III)-amended soils. Analyses of potential function (GeoChip) revealed higher abundances of metal-resistance genes in metal-rich soils. For example, genes that hybridised with metal-resistance genes copA, chrA and czcA of a prevalent aurophillic bacterium, Cupriavidus metallidurans CH34, occurred only in auriferous soils. These data help establish key links between geogenic factors and the phylogeny and function within soil microbial communities. In particular, the landform, which is a crucial factor in determining soil geochemistry, strongly affected microbial community structures.
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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 | 0.000 | 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) | 0.000 | 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".