Sustainable Interdisciplinary Transformation of Warsaw University of Technology Buildings. KODnZEB Case Study
Bibliographic record
Abstract
Sustainable development has by now become an element deeply integrated in everyday design. The main difficulty is not the design of a new building but transformation and modernization of existing ones. Warsaw University of Technology (WUT) is one of the oldest universities in Poland, its building dating back to the beginning of the twentieth Century. It is spread over several sites most of whichboth the urban layout as well as many buildings -are under the care of historic preservation authorities. This substance should in the future years become one of the basic issues fulfilling Effective Energy Directive -lowering of the energy needs in the construction sector. This procedure is much easier when dealing with new buildings, not those existing and undergoing modernization. In 2015, a Nordic Finance Mechanism project for the nZEB technology transfer from Norway to Poland was awarded to a group of researchers from WUT and NTNU Trodheim. The main aim of the project is implementation of nZEB knowledge in Poland, as well as the preparation of two integrated concept designs for public (University) buildings as exemplary case studies that could act as benchmarks for other public buildings. The transfer of technology is not easy, both due to economic limitations, as well as different technical requirements, which have to correspond with Polish Building Codes. The other issue being that the Integrated Design Process is not very much used in Poland and, therefore, procedures and management of the project will form part of the transferred know-how. The benchmark public buildings belong to WUT -one is a student dormitory, the other houses the Faculty of Building Services, Hydro and Environmental Engineering. Both buildings date back to the 1970s. The outcomes of the project will also include the compilation of a Proceedings Manual dedicated to possible public investors showing an Integrated Road Map, including legal, financial and technical issues -and allowing choosing a best-case scenario. Training workshops are also foreseen within the project -they will take place in different parts of Poland and will start with a 'train the trainers' meeting, who in turn will be able to implement knowledge in other regions than just Warsaw. We also hope that we will be able to secure a construction grant -for the modernization of at least one of the chosen 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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".