Cost optimal analysis of energy refurbishment actions depending on the local climate and its variations
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 cost optimal methodology indicated by the European Directive 2010/31/UE represents the starting point to support decisions for refurbishment interventions. The procedure is much more useful if considering the limited available investments from Public Authorities or privates and represents an important decision tool to define the owners' constraints and the economic commitment in the years. However, the results are usually affected by the influence of some variables, among which there are the climatic conditions, subject of the present investigation. To quantify some effects, the best costs/benefits ratio is evaluated among some improvement scenarios built on a series of energy efficiency measures, in the climatic conditions of some locations. The first set of calculations compares energy performance and global costs referring to climatic data taken from the Italian National Standard UNI 10349 edition 1994 and 2016.
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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