A novel spherical approach for the definition of assets, interdependencies and interconnections to investigate needs for the protection of maritime heritage sites
Bibliographic record
Abstract
The THETIDA project addresses issues related to safeguarding and protection of Europe’s coastal and underwater cultural heritage (CH) from the effects of climate change and natural hazards. It does so in a holistic manner that includes risk management, protection and preparedness, as well as complementary strategies. The aims are to prevent damages to CH sites, identify and ward off additional threats and promote policy tools for climate neutrality and economic resilience in coastal areas. To this end, the project focuses on 7 pilot sites with a significant archaeological and historical interest from 6 different European countries (Greece, Portugal, Italy, Norway, The Netherlands and Cyprus). In this framework, the interdisciplinary team will develop, test and validate an integrated multiple heritage risk assessment and protection system; it will incorporate evidence-based monitoring frameworks, innovative tools and instruments and participatory processes (Citizens’ Science and Living Labs). Thus, the project implementation actions will link the social innovations with cutting-edge technologies (Information and Communications Technology and Internet of Things harmonised tools). To achieve this, an in-depth analysis of the CH and the interconnected non-CH assets of the selected pilot sites was developed in a structural, environmental and user level. The poster outlines the classification process, taking into consideration the uniqueness of each site. That is the state of preservation of the archaeological assets, the surrounding environmental and weather conditions, as well as the marine organisms and other anthropogenic factors affecting the sites. Census data that will map permanent residents and seasonal visitors so to identify the inter-/intra-core interactions of local operators, is also gathered. Mapping and classifying these agents can assist significantly to the assessment of each site and to the selection of the necessary monitoring tools. It can also provide the necessary data for building a model of the socioeconomic fabric of the CH core to develop a sustainable way for the site’s protection.AKNOWLEDGMENTSThis research has been funded by the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101095253, THETIDA project (Technologies and methods for improved resilience and sustainable preservation of underwater and coastal cultural heritage to cope with climate change, natural hazards and environmental pollution).CMMI was established as a “Center of Excellence” in Marine and Maritime Research, Technology Development & Innovation (RTDI) and has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 857586 and matching funding from the Government of the Republic of Cyprus.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".