North and South: Exploring isotopic analysis of bone carbonates and collagen to understand post‐medieval diets in London and northern England
Bibliographic record
Abstract
Abstract Objectives We evaluate the potential of paired isotopic analysis of bone carbonate and collagen to examine the diet of post‐medieval human and animal populations from England (17th–19th c.), including, for the first time, manufacturing towns in northern England. The potential for identifying C4crop consumption is explored alongside regional and local patterning in diet by sex and socioeconomic status. Materials and Methods Humans (n = 216) and animals (n = 168) were analyzed from sites in London and northern England for both carbon and nitrogen isotopes of bone collagen (𝛿13Ccoll, 𝛿15Ncoll). Isotopic analysis of bone carbonates (𝛿13Ccarb, 𝛿18Ocarb) was carried out on all humans and 27 animals, using Fourier transform infrared spectroscopy–attenuated total reflectance to assess diagenesis. Results Variations in diet were observed between and within different populations by geographical location and socioeconomic status. Three pigs and one cow consumed C4resources, indicating the availability of C4‐fed animal protein. Londoners consumed more animal and marine protein and C4resources. Middle‐ and upper‐class populations from both London and northern populations also had greater access to these foods compared to those of lower status in the same regions. Discussion This substantial multi‐isotope dataset deriving from bone carbonate and collagen combined from diverse post‐medieval urban communities enabled, for the first time, the biomolecular identification of the dynamics of C4consumption (cane sugar/maize) in England, providing insight into the dynamics of food globalization during this period. We also add substantially to the animal dataset for post‐medieval England, providing further insight into animal management during a key moment of agricultural 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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".