Typological Study of the Building Heritage on a GIS Platform to Support Territorial Energy Planning Measures
Bibliographic record
Abstract
This research was aimed at the implementation of an Urban Building Energy Model, based on an open GIS digital platform, acting as a support for the definition of energy efficiency strategies and recovery of the urban building heritage, specifically for medium-sized contexts with a Mediterranean climate. To guarantee its maximum replicability, the model involves the use of all data commonly available at regional/municipal level (topographic bases, Urban and Detailed Plans, ISTAT data, architectural constraints, etc.), focusing on the public and private buildings of the Municipality of Carbonia. The basic calculation methodology is the one regulated by the technical standards of the sector (UNI TS 11300 and UNI EN ISO 52016). The substantial differences compared to the UBEM models already present in the literature are both the inclusion of other parts of the energy systems (generation and distribution systems) and the use of internal comfort data obtainable from the monitoring system. Another intrinsic peculiarity of the model is its hierarchical structure coded in Python language, capable of displaying and comparing data coming from sensors with standard threshold values, allowing stakeholders and/or individual owners, at the level of the single building, a more informed choice of the advantages connected to the possible intervention scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".