Archaeological soil from Roman occupational layers can be differentiated by microbial and chemical signatures
Why this work is in the frame
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Bibliographic record
Abstract
Introduction Soil at the Roman site of Vindolanda (Northumberland, UK) provides excellent preservation of wooden artefacts including Roman writing tablets. Methods In this study we examined chemical and microbial signature changes within varied occupation contexts of archaeological soil. Analysis included investigating elemental composition, sterol biomarkers, bacterial diversity and community structures from excavation trenches at Vindolanda using pXRF, GC-MS and 16S rRNA gene amplicon sequencing. Samples were taken from varying depths starting at topsoil and working down through layers of Roman occupation including one cavalry stable floor, two infantry barracks and a cook house, and layers which contained Roman writing tablets. Results and Discussion The chemical results indicate that areas where wooden artefacts were found had increased soil moisture which was also correlated with specific chemical conditions including shifts in iron, sulphur and phosphorous concentration. Steroid biomarkers indicate the presence of faecal matter in layers, supporting occupation descriptions. Overall microbial diversity did not change across the depth profile but was correlated with soil moisture. Anaerobic soils associated with more optimal preservation differed to other soils with increases in Firmicutes, Proteobacteria, Campilobacterota and Bacteroidota observed. Microbial community structure and putative function as revealed by PICRUSt2 is linked to occupation usage rather than depth of samples with laminated floor layers differing from turf structures. Understanding the complex processes within archaeological soil can help us to understand dynamics of decomposition and preservation. In addition, the apparent preservation of the environmental microbial community as well as the artefacts themselves allows us to understand the microbial environments of the past, how they relate to the present and what this means for our changing environments in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it