Cityopt planning tool for energy efficient cities
Bibliographic record
Abstract
There are many ways to integrate components (renewable energy sources, storages and energy efficient buildings) into a sustainable district or city and various corresponding urban strategies.However, the best solution is not always straightforward, and simulation tools are needed to select the optimal design according to specific criteria.The objective of the CITYOPT project is to create tools to support planning, designing and operating sustainable energy solutions in cities.In particular, the CITYOPT planning tool will support simulating, optimizing and analyzing various city planning alternatives.This holistic approach will integrate, among others, energy dynamics of local grids, buildings and consumption behavior and patterns, energy storages, and local energy production using renewables.The results from test use of the tool are presented alongside with business models for the case areas.For the Vienna case, the CITYOPT planning tool will allow to assess different possible designs for an industrial waste heat-based micro district heating network, supplying low-energy buildings.Existing renewable energy solutions and thermal storages (long and short term) are also considered.The Helsinki case consists of electricity storage solution planned in Kalasatama district and a combination of sustainable heating solutions planned for the Östersundom district.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.011 |
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".