TOWARDS BUILDING A SEMANTIC FORMALIZATION OF (SMALL) HISTORICAL CENTRES
Bibliographic record
Abstract
Abstract. Historical small urban centres are of increasing interest to different interacting fields such as architectural heritage protection and conservation, urban planning, disaster response, sustainable development and tourism. They are defined at different levels (international, national, regional), by various organizations and standards, incorporate numerous aspects (natural and built environment, infrastructures and open spaces, social, economic, and cultural processes, tangible and intangible heritage) and face various challenges (urbanization, globalization, mass tourism, climate change, etc.). However, their current specification within large-scale geospatial databases is similar to those of urban areas in a broad sense resulting in the loss of many aspects forming this multifaceted concept. The present study considers the available ontologies and data models, coming from various domains and having different granularities and levels of detail, to represent historical small urban centres information. The aim is to define the needs for extension and integration of them in order to develop a multidisciplinary, integrated semantic representation. Relevant conventions and other legislation documents, ontologies and standards for cultural heritage (CIDOC-CRM, CRMgeo, Getty Vocabularies), 3D city models (CityGML), building information models (IFC) and regional landscape plans are analysed to identify concepts, relations, and semantic features that could form a holistic semantic model of historical small urban centres.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".