Градостроительное развитие территорий Приневья до основания Санкт-Петербурга: Водская пятина и Ингерманландия
Bibliographic record
Abstract
On the basis of the Old Russian chronicles (8th–14th centuries), medieval Scandinavian texts (10th–14th centuries), Inventory Books of Novgorod, Moscow and Swedish periods (15th–17th centuries), the Swedish cartography (17th century), the issues of identification of the settlement distribution system on the territories along the Neva River, around the Ladoga Lake and the zone of the Gulf of Finland during the period before the foundation of St. Petersburg are considered in the article. The picture of formation and sustainable development during several centuries of the rural settlement distribution system including thousands of settlements and numerous versts (Russian measurement units) of roads is shown. Spatial and planning features of historical system of settlements had mainly North Russian nature of “nest-type construction”. Hundreds of settlements and thousands of kilometers of roads were included from 1703 to 1712 into the planning structure and quarter — sloboda (rural settlement) fabric of the capital city of St. Petersburg and its residential suburbs. Thereby, from the times of Peter I, a large-scale reconstruction of the historical settlement distribution system was carried out on the territories along the Neva River, turning the city into the urban capital agglomeration of the regular type. All this enables us to completely reject the established mythology about creation of St. Petersburg from scratch, without taking into account the historical spatial heritage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".