Biogeography of soil Thaumarchaeota in relation to soil depth and land usage
Bibliographic record
Abstract
Although Thaumarchaeota are important contributors to ammonia oxidation in terrestrial habitats, distributions of ammonia oxidizers along soil depth profiles are poorly understood, especially in relation to distinct land usages. Leveraging the close proximity of forest, field and agricultural plots at the RARE: Charitable Research Reserve, we examined soil thaumarchaeotal biogeography at three different depths (0-15, 15-30 and 30-45 cm) from plots within areas of contrasting land usage. Data from high-throughput sequencing of thaumarchaeotal 16S rRNA gene sequences demonstrated that OTU richness was affected significantly by depth and land-use type. Specifically, thaumarchaeotal diversity was higher in soils from forest sites than from field sites, and lower within 0-15 cm soils than either 15-30 cm or 30-45 cm soils. Soil land-use type influenced the relative abundance of the Soil Crenarchaeota Group (SCG), with a lower relative abundance of SCG in forest sites compared to field sites. At the OTU level, thaumarchaeotal communities changed with increasing soil depth for agricultural soils, in contrast to homogeneous depth profiles generated from forest site samples. Soil pH was the strongest factor impacting thaumarchaeotal community composition and, importantly, the evenness of archaeal taxa. Nitrogen, carbon and soil texture shaped thaumarchaeotal community composition among field site samples.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".