DIGITAL MODELS FOR THE ANALYSIS AND ENHANCEMENT OF HYBRID SPACES: ARCHITECTURE OF THE MATTANZA
Bibliographic record
Abstract
Abstract. Project ‘U Mari examines the long-term relationship between the sea, coast, and local peoples through the various lenses of maritime mobilities, interactions, and livelihoods along the shore of southeast Sicily, specifically between the Vendicari Reserve and Capo Passero. With an eye toward valorizing the ‘mattanza’ as intangible cultural heritage, our work focuses on the rich material remains of this distinctive Mediterranean form of bluefin tuna trap fishing, using 3D recording and visualization of its associated objects, spaces, and landscapes to relate vivid diachronic stories for the public. Our methodology integrates archaeological survey of the landscape, architecture, and social practices of tuna fishing that act as a bridge between ancient, early modern, and contemporary livelihoods. Through comprehensive digitization, we generate interoperable and parametric models aimed not only at the recording and restoration of objects and spaces, but also – in combination with interviews and archival work – at the valorization and revitalization of traditional practice within contemporary socioeconomic contexts. Through these digital methods, Project ‘U Mari seeks to engage the public with a deeper understanding of historical maritime lifeways using exhibition, virtual environments, and revived traditions. Such an approach can encourage environmentally sound fishing practices that draw on local knowledge and yield local economic benefits and responsible tourism. In this way, the historic and archaeological past offers the opportunity to create a new common language for understanding and communicating the architectural evidence of local traditions, history, and livelihoods in this rich maritime landscape.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".