New Perspectives on Migration into the Tlajinga District of Teotihuacan: A Dual-Isotope Approach
Bibliographic record
Abstract
The city of Teotihuacan (AD 1–550) was a major multiethnic urban center that attracted migrants from as far away as west Mexico and the Maya region. Past research in the Tlajinga district at Teotihuacan using oxygen isotopes from human remains estimated that nearly 30% of the population of Tlajinga 33, a single apartment compound, were migrants. This study takes a dual-isotope approach (87Sr/86Sr and δ18Op) to reevaluate the proportion of in-migration at Tlajinga and includes data from two additional apartment compounds, Tlajinga 17 and 18 (n = 23). New results indicate that migrants comprised ~45% of the Tlajinga population. Previously acquired radiocarbon dates combined with mortuary and isotope data suggest that immigration to Tlajinga was highest during the first centuries of compound occupation. Nevertheless, migration was a continual process throughout its history. Additionally, a new finding suggests that residents of Tlajinga 33 ingested foods with higher 87Sr/86Sr ratios than did those of Tlajinga 17 and 18. We hypothesize that the incorporation of imported lime for the nixtamalization process skewed the 87Sr/86Sr ratios of human remains, a potentially important finding for future studies at Teotihuacan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".