Emerging strontium isoscapes of Anatolia (Türkiye): new datasets and perspectives in bioavailable 87Sr/86Sr baseline studies
Bibliographic record
Abstract
Introduction The use of strontium isotope ratio (87Sr/86Sr) analysis in ancient mobility studies in the archaeology of Anatolia (modern Türkiye) has steadily grown since the early 2010s. However, a coherent map of the isotopic variability of bioavailable Sr (isoscape) does not exist for the region and the paucity of baseline data that is necessary for the interpretation of archaeological data significantly constrains the heuristic power of this methodology in Anatolian archaeology. Baseline and “local range” determination in previous studies have relied on geology maps or various sample types from very limited areas in site-centered mobility studies, and the use of predictive modeling for isoscape reconstruction at regional scales has just begun in Türkiye. Methods In this study, we discuss current methodologies in Sr isoscape reconstruction including the recent open-access R-script and global database developed for modeling bioavailable 87Sr/86Sr isoscapes and we present a review of extant 87Sr/86Sr analyses and baseline studies in Anatolian archaeology. We combine all published baseline 87Sr/86Sr data from Türkiye with our unpublished 87Sr/86Sr data from proxy samples (plants and snail shells) from central Anatolia, and by incorporating this data (n = 688) into the global database (where data from Türkiye is currently lacking), we create a modeled 87Sr/86Sr isoscape of Türkiye utilizing the R-script and we calculate the predicted standard error for this isoscape. Results and discussion This study demonstrates how additional empirical data serves to improve the Türkiye section of the global model using kriging and random forest regression (RFR) techniques and it discusses how the uneven distribution of data impacts the resultant isoscape map. In closing, we comment on beneficial avenues for mobility studies in under-researched periods and regions in Anatolia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".