SEMANTICALLY DESCRIBING URBAN HISTORICAL BUILDINGS ACROSS DIFFERENT LEVELS OF GRANULARITY
Bibliographic record
Abstract
Abstract. Architectural, built heritage and historical buildings embody cultural heritage value and - as known - they need to be studied, documented, persevered and represented. Although there are many fields involved in these activities, none of these considered individually can fully represent the heritage with a complete level of detail and information. The present work aims to investigate the different levels of detail and granularity among different communities involved in historical buildings tasks to semantically define different concepts. In this context, ontologies are considered as an effective solution for the formal conceptualization of the domains involved, providing a common language for knowledge sharing and reuse. The study starts from existing knowledge (standards, vocabularies, thesauri, classifications) and conceptualisations for regional, urban and architectural heritage and geographic information for various tasks (restoration, documentation and heritage studies, risk prevention, heritage asset and facility management, education and tourism, urban planning and energy refurbishment/performance). A specific use case involving historical buildings in fortified centres across different levels of detail is described to show how existing knowledge and standards conceptualisation need to be integrated and extended.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".