Development of the Land Transport System of the Rural Settlement Network in the Tomsk Uyezd (District-Union) in the Second Half of the 19th Century and the First Quarter of the 20th Century
Bibliographic record
Abstract
The process of development of the land transport system — postal and zemstvo tracts, volost roads, railway communication is considered. The degree of influence of the transport component on the development of the rural settlement network of the Tomsk Uyezd (district-union) in the Tomsk Province in the second half of the 19th — first quarter of the 20th century is estimated. The author shows the role and significance of the Moscow-Siberian, Tomsk-Kuznetsk, Tomsk-Barnaul, Barnaul-Kolyvansky, Narymsky tracts, local volost roads, as well as the Trans-Siberian Railway on the process of formation of a group of rural settlements of peasant and “foreign” volosts of the Tomsk Uyezd. The work focused on cartographic materials of the corresponding period, as well as a schematic cartographic model of the key transport communications of the northern, central and southern groups of parishes. According to the author, the basis of the land transport network was formed at the turn of the 19th-20th centuries. In the 1920s, there was only a modernization of the already established lines of communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".