Mapping the origins of Imperial Roman workers (1st–4th century CE) at Vagnari, Southern Italy, using <sup>87</sup>Sr/<sup>86</sup>Sr and δ<sup>18</sup>O variability
Bibliographic record
Abstract
Abstract Objectives We obtained the oxygen and strontium isotope composition of teeth from Roman period (1st to 4th century CE) inhabitants buried in the Vagnari cemetery (Southern Italy), and present the first strontium isotope variation map of the Italian peninsula using previously published data sets and new strontium data. We test the hypothesis that the Vagnari population was predominantly composed of local individuals, instead of migrants originating from abroad. Materials and Methods We analyzed the oxygen (18O/16O) and strontium (87Sr/86Sr) isotope composition of 43 teeth. We also report the 87Sr/86Sr composition of an additional 13 molars, 87Sr/86Sr values from fauna (n = 10), and soil (n = 5) samples local to the area around Vagnari. The 87Sr/86Sr variation map of Italy uses 87Sr/86Sr values obtained from previously published data sources from across Italy (n = 199). Results Converted tooth carbonate (δ18ODW) and 87Sr/86Sr data indicate that the majority of individuals buried at Vagnari were local to the region. ArcGIS bounded Inverse Distance Weighting (IDW) interpolation of the pan‐Italian 87Sr/86Sr data set approximates the expected 87Sr/86Sr range of Italy's geological substratum, producing the first strontium map of the Italian peninsula. Discussion Results suggest that only 7% of individuals buried at Vagnari were born elsewhere and migrated to Vagnari, while the remaining individuals were either local to Vagnari (58%), or from the southern Italian peninsula (34%). Our results are consistent with the suggestion that Roman Imperial lower‐class populations in southern Italy sustained their numbers through local reproduction measures, and not through large‐scale immigration from outside the Italian peninsula.
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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 | 0.001 |
| 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 | 0.000 | 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".