Bibliographic record
Abstract
С начала XXI века в Китае появилось несколько знаковых проектов реконструкции исторической застройки. Одним из первых стало предложение по реконструкции двух старых улиц Куай-сянцзы и Чжай-сянцзы в городе Чэнду провинции Сычуань, где основной упор был сделан на сохранение исторической застройки улиц и местного колорита. Затем появился знаменитый проект перестройки квартала Тайгули в Чэнду, который пошел по иному пути — по пути создания новой национальной архитектуры современными средствами. А в проекте реконструкции квартала 960 города Кайфэн провинции Хэнань архитекторам нужно было учитывать наличие на участке ценных объектов археологии, что повлияло на выбор легких конструкций для новой застройки и тем самым в значительной степени определило ее облик. Рассмотренные в данной статье проекты решали различные градостроительные задачи, но цель у них была схожа — создание комфортной современной среды исторического города. A number of significant projects for the reconstruction of historical urban areas appeared in the early twenty-first century in China. One of the first was the reconstruction project of Kuai-xiangzi and Zhai-xiangzi streets in Chengdu city, Sichuan province. This project was mainly focused on preservation of historical buildings and local style. Thereafter a famous redevelopment project of Taikooli was implemented in Chengdu city, which had different approach of inventing a new contemporary national architecture. In a third project of reconstruction of 960 quarter in Kaifeng city, Henan province architects had to consider a presence of valuable archaeological objects at the site, which led to the use of light constructions for new buildings and highly affected their appearance. Projects reviewed in this paper solved various urban tasks, but they had a similar goal of creation of modern and comfortable environment of historical city.Keywords: reconstruction, historical urban areas, urban public space, China.
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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".