Assessing the applicability of protein residues in combination with lipid residues to reconstruct Indus foodways from Gujarat
Bibliographic record
Abstract
When extracted and analysed in tandem, proteomics and lipid residue analysis can provide high resolution identification of ancient foodstuff. Here, we apply lipid residue and shotgun proteomic analyses to 11 ceramic vessel sherds from the Gujarat, India-based Indus Valley Civilization site of Kotada Bhadli. Our results demonstrate variable success. Lipids were successfully recovered from each ceramic vessel and suggest the presence of dairy and meat from cattle/buffalo, and meat from sheep/goat and monogastric animals, such as pigs and birds. Additionally, we were also able to identify the presence of plant products such as leafy vegetables, oils and broomcorn millets. In contrast, none of the extracted proteins could be confidently traced to specific foods or ingredients and were thus unable to contribute to broader interpretations of foodways at Kotada Bhadli. Nevertheless, our results present an opportunity to discuss pathways for improving proteomic methods, and advocate for the need to report negative results as well as positive ones. We support continued efforts to apply multi-proxy approaches to the study of ancient ceramics and consider future applications of shotgun proteomics in this rapidly evolving field.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".