Study on Optimization Strategy of Urban Residential Quarter Dealing with the Climate Change in Winter Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, climate change has been getting more serious. How to mitigate and adapt to climate change has caught the concerns of governments and academia. Firstly, this article briefly addresses the causes of climate change and its impacts, and then analyzes the link between climate change and urban settlements and the impacts of climate change to urban settlements in winter city. Finally, according to the Characteristics of winter city, the paper presents some optimization strategies of urban residential quarter in winter city addressing climate change including reducing carbon emissions, ensuring settlements security and guiding residents to public participation. Reducing urban settlements carbon emissions includes improving internal functions, combing the internal transportation system, optimizing the green mode and applying special techniques. Protecting the safety of urban settlements includes improving emergency response system, strengthening the vertical and horizontal connection and optimizing the layout of public space. Guiding residents to public participation includes establishing the information banks of urban settlements addressing to climate change and improving the quality of the residents.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it