Necropolises and Burial Sites on the Island of Sviyazhsk Case Study of Archaeological Data
Bibliographic record
Abstract
The archaeological explorations on the island of Sviyazhsk, which are conducted more actively in recent years, allowed discovering the complicated history of ancient days hidden and buried deep in the ground of the island which in the mid-fifties was completely cut off from the modern world by the waters of the Kuibyshev reservoir. Mapping of the necropolises discovered during archaeological, construction and restoration works is important component of the set of actions aimed at complex studying and preservation of cultural heritage sites. Besides abounding material culture, archeologists managed to track the planning features of the development of Sviyazhsk island mountain of the late Middle Ages and Modern Age including those reflected in the arrangement of the monastic and the parish cemeteries. The article provides data on localization of the necropolises discovered during archaeological researches of 2008-2010 on the island of Sviyazhsk. The Orthodox necropolises and individual burial sites under study date back to the XVI-XX centuries. The study materials are of great practical importance for the reconstruction of historical planning and establishment of the memorial site to bring out tourist potential, and to conduct scientific researches of the historical and demographic processes in the Middle Volga region.
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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 | 0.004 | 0.004 |
| 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) | 0.003 | 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".