THE MISSISSIPPIAN FIN DE SIÈCLE IN THE MIDDLE CUMBERLAND REGION OF TENNESSEE
Bibliographic record
Abstract
Bayesian chronological modeling is used to investigate the chronology for a large-scale human depopulation event during the Mississippian period (AD 1000–1600) known as the Vacant Quarter phenomenon. The Middle Cumberland region (MCR) of Tennessee is within the Vacant Quarter area, and six villages from the final phase of Mississippian activity in the MCR have been subjected to radiocarbon dating. Complete radiocarbon datasets from these sites are presented within an interpretative Bayesian statistical framework. The results provide a unique history of each settlement and demonstrate that Mississippian occupations at each site likely terminated in the mid- to late fifteenth and possibly early sixteenth centuries AD, which is 50 to 100 years later than the most recent estimate for the timing of the Vacant Quarter. Mississippian abandonment in the MCR was relatively quick, likely occurring over less than a century. The exact reasons for abandonment are not entirely clear but appear to be linked to climate change. A radiocarbon simulation experiment indicates that future robust radiocarbon dating with well-selected samples could greatly improve the chronological precision for this late Mississippian activity. More broadly, this example demonstrates that model building with radiocarbon simulations can be used to address regional-scale chronological issues within the American Southeast and beyond.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".