The Roman way: Investigating the cremation conditions during the Roman period in Belgium using a multi-proxy and multi-sampling approach
Bibliographic record
Abstract
This study assesses cremation conditions in the Roman period using a multi-proxy analysis (FTIR-ATR and carbon and oxygen isotope analysis) on 332 burned bones from five Belgian Gallo-Roman cemeteries. The results suggest similar pyre structure, size, temperature, and body positioning across Gallo-Roman cremations. However, high variability in δ 13 C and δ 18 O values indicates differences in fuel selection and environmental factors. The wide δ 13 C range likely reflects the use of multiple wood types (e.g., Quercus sp./oak, F. sylvatica/beech) and different tree parts (e.g., trunk, branch, stump) in pyre construction. In contrast, δ 18 O variation may relate to quenching methods and/or seasonal and weather conditions during combustion. Differences were also observed in cremation conditions between the Metal Ages and the Gallo-Roman cremations from Belgium, with Roman cremations presenting better oxygen availability during combustion. Finally, the Gallo-Roman cemetery of Fouches is particularly interesting, as it dates to the Early Roman period and presents similarities in ventilation conditions with the cemeteries from the Metal Ages instead of the other Gallo-Roman cemeteries. The evidence from Fouches suggests a gradual transition from the Metal Ages to Roman cremation practices. The dating of Fouches to the Early Roman period could potentially explain that Roman cremation expertise was not immediately widespread but rather transferred gradually to the edges of the Roman Empire.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.000 |
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".