The Main Street of Tver in the 17th — Mid 18th Centuries
Bibliographic record
Abstract
The main street of Tver originates in the far Middle Ages, but a relatively clear idea of this “highway” may be traced only starting from the 17th century, when the development and urban planning structure of the Russian city, in accordance with peculiar views of reality, were reflected in the works of icon painters, as well as foreign travelers. The era of Peter I strengthened the documental use when depicting the layout of a late medieval city, yet the fixation of urban planning structure was largely spared from instrumental survey, since domestic cartographers of the first quarter of the 18th century, as a rule, preferred to focus on the iconographic tradition. Residential, religious and other urban objects are represented in these materials in quite a schematic and sometimes generalized way. The situation changed by the middle of the 18th century, but this period (the second quarter — the middle of the 18th century) in the cartographic heritage of Tver still remains a blank spot. For this reason, it is currently quite difficult to outline the route of the city’s main highway in the pre-Catherine era, since in the mid-1760s the direction and configuration of the main street of Tver were changed by the initiative of Catherine II. Nevertheless, this study attempts to reconstruct the location of the pre-Catherine “highway”, as well as to present the nature of almost completely lost urban development associated with this street in the late Middle Ages and in the first half of the 18th century.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".