Australian dryland soils are acidic and nutrient‐depleted, and have unique microbial communities compared with other drylands
Bibliographic record
Abstract
AIM: To compare Australian dryland soils with dryland soils globally. LOCATION: Australian and global drylands. METHODS: We used data from standardized surveys of soil properties (C:N:P content and stoichiometry, and pH), and microbes (diversity, composition and correlation networks) from Australian and global drylands, which occupy three-quarters of the Australian land mass and are the largest biome on Earth. RESULTS: We found that Australian dryland soils were different, exhibiting characteristics of ancient weathered soils. They had lower pH, total and available P, and total N, and greater C:N and C:P ratios than global dryland soils. Australian soils had distinctive microbial community composition and diversity, with more Proteobacteria and fewer Basidiomycota than global dryland soils, and promoted the abundance of specific microbial phylotypes including pathogens, mycorrhizae and saprobes. MAIN CONCLUSIONS: Australian dryland soils are clearly different from dryland soils elsewhere. These differences need to be considered when managing dryland soils to avoid unreasonable expectations about plant productivity and carbon stocks, or when predicting likely changes in ecosystem processes resulting from global environmental change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".