Малые улицы исторических городов: музейные характеристики и перспективы музеефикации
Bibliographic record
Abstract
There are lots of museums located in small streets, but the cultural space of the streets themselves has the characteristics of a museum it is no accident that small streets have always been an object of attraction for artists. The most important cultural characteristics of small streets are the unique geometry, which is the result of the natural urban development, and numerous historic buildings. Architecture suggests restoration and reconstruction methods of preserving the historic environment, but to include this environment in the contemporary multi-functional cultural space, an interdisciplinary approach is necessary. It is most important to develop certain computer technologies which would be able to include the museum in the space of the media and the web. Small streets museefication is the continuation of the reservations of 1970s 1980s. It is an environmental museum preserving the natural urban functions of a city. It is suggested to keep the museeficated space of small streets within the confines of the medieval settlements of Russian historic cities. A museeficated small streets quarter is another new type of museum. The functions of the museum are evenly distributed, but in certain locations they concentrate.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".