Techniques of Improving Infrastructure and Energy Resilience in Urban Setting
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
The work proposes a technique to improve the infrastructure and energy resilience of new developments during the planning stage. Several resilience-related parameters are developed in this paper that can be used to quantify resilience. To apply these parameters, the work assumes various energy outage scenarios varying from less than 24 h to 3 weeks. During these scenarios, a neighborhood population can be relocated to several public buildings promoting better utilization of onsite energy resources. The technique is applied to four representative neighborhoods encompassing various sustainability measures including clean energy. Further, this paper demonstrates an urban scale improvement technique for greater energy and infrastructure resilience. The results indicate a significant improvement in infrastructure resilience by relocating public shelter buildings on the main street intersections so that these can be easily accessible during energy outages or disaster events. Energy resilience can be achieved by the appropriate design of onsite energy resources to eliminate vulnerabilities. For instance, 8.8% to 15.4% of additional land for solar thermal collectors can eliminate thermal energy vulnerabilities. When surplus generation from onsite resources is twice or more as compared to demand during their unavailability, the electrical vulnerability can be eliminated by employing suitable battery banks in various buildings.
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