Bibliographic record
Abstract
In their article, author considers the historical experience of introducing new methods of road paving into the practice of road maintenance in St. Petersburg. Obviously, this example is of interest to modern city authorities. The purpose of the study is to determine the degree of development of the asphalt production industry and methods of attracting firms and companies to manufacture road surfaces in the urban environment of the Russian Empire. The topic has practically not been considered by Russian and foreign historians. The source base for the article is documentary materials from St. Petersburg archives. The information on the activities of enterprises producing asphalt and road works was taken from the funds of the St. Petersburg office of the State Bank of Russia, the city government of the capital, and the boards of asphalt enterprises. Modern methods of paving Russian city streets have had relatively brief history. They began to be widely introduced into the urban environment in large economic centers of pre-revolutionary Russia only in the last quarter of the XIX century. The first such pavements appeared in St. Petersburg, Moscow, Warsaw, Odessa and other Russian cities during that period. It should be noted that due to various reasons the asphalt production industry in the Russian Empire was poorly developed, by the First World War, it was in deep crisis. Subsequent events in Russia definitively scaled back the asphalt production in the country, which was revived only by the 1930s.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".