Development of urban areas of the Russian Federation on the Trans-Siberian Railway: town-planning a railroad role in the city of Krasnoyarsk.
Bibliographic record
Abstract
Not populated or low-populated territories development due to the railroads construction exerted considerable impact on regional planning of the country. Construction of the Great Siberian way – the Trans-Siberian Railway was one of the significant events of the end of the 19th century. Numerous new settlements and the cities, such as Novosobirsk, Irkutsk, Krasnoyarsk appeared on the map of the country. Krasnoyarsk began to develop violently and grew by 270% after railroad construction in 1897 - 1911 years. New created structure of resettlement entailed industry development. A lot of the new productions were transported from the central part of the country during the Second World War. Factories were accommodated along the railroad generally. The majority of objects of cultural railway heritage remained on the railroads territories in present time. The pioneer settlement of railroad workers in the city of Krasnoyarsk – is the Nikolaevskaya sloboda escaped. Typically Siberian residential buildings and style life still characteristic for that unique area. The strategies of the renovation of the area Nikolaevskaya sloboda oriented toward the tourist quarter of the Siberian city is required. Development of the city continues. Krasnoyarsk, thanks to the railroad, became the million plus city. Light rail transport, rewatching municipal warehouse territories under cultural clusters, business and residential districts is supposed in the future. Development process is oriented to transformation of the transport oriented district (TOD).
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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 | 0.001 |
| 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".