A draft decision on the issue of urban expansion of a densely populated city
Bibliographic record
Abstract
Abstract The problem of urbanization turns up from the urban compaction. This project is designed to solve this problem in the densely populated city of Tokyo, the population of which is more than 13 million people today. The project of a new neighborhood in the city takes into account social, economic, environmental and geographical characteristics of the Land of the Rising Sun. The project provides one to combine the idea of a universal city with high population density in a small territory. The territory involves a residential area, an industrial area and an agricultural area simultaneously that promotes well-balanced relations between man and nature. The purpose of the survey is to show that it is necessary and possible to use a new strategy of city planning and building, relevant for large, densely populated cities, to make cities more comfortable and economical. The quarter is located on a man-made island in Tokyo Bay. The quarter was built after burning solid household waste without causing damage to the environment by a waste burning plant located on this island to the north. In addition, the plant produces gravel material for creating an artificial island. Besides, it yields surplus electric energy that is fed into the power grid of Tokyo.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.031 | 0.022 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".