Leave no stone unturned: Exploring the metaproteome of beerstone for the identification of archaeological beer production
Bibliographic record
Abstract
In archaeological contexts, identifying processes of beer production and consumption has contributed to our understanding of agriculture, labor mobilization, economic surplus, feasting, gender dynamics, social structure, tribute, community, identity and politics. Nevertheless, in the absence of pictorial representations and characteristic objects, beer brewing is difficult to identify in the archaeological record, and molecular methods are often limited by constraints of preservation and specificity. A potential target for studies of ancient beer production are residues formed during brewing activity, including beerstone, a calcium oxalate residue. Here, we apply shotgun proteomics analyses to a sample of modern beerstone to explore this residue's capacity as a marker for beer in archaeological contexts. The beerstone proteome was compared to the protein profiles of ungerminated and germinated barley to identify key proteins indicative of malted grains which may be encased by the residue. Proteins matching to barley grain (Hordeum vulgare) and Baker's/Brewer's yeast (Saccharomyces cerevisiae) were successfully identified in the beerstone. In particular, we identified hordeins, lipid transfer proteins, trypsin/α-amylase inhibitors, and protein Z, which are barley proteins abundant in proteomic characterisations of beer. In comparison to ungerminated and germinated barley grains, we find that beerstone preserves only a subset of the barley proteome, with the residue being more reflective of the final brewing product than of earlier brewing steps such as malting. Overall, we demonstrate that beerstone has potential to entrap and preserve proteins reflective of the beer-making process and identify proteins that we might anticipate in future archaeological analyses.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".