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Smart infrastructure and digitalization: content analysis of METRO'24 RockLine, Slovakia

2025· article· en· W4412385382 on OpenAlexaff
Farhad Nazir, Jan Michalík

Bibliographic record

VenueManagement of Development of Complex Systems · 2025
Typearticle
Languageen
FieldEngineering
TopicTransport and Logistics Innovations
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsTransport infrastructureEnvironmental scienceBusinessTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Smart infrastructure has become a key aspect of urban development, driven by digitalization and sustainability goals. The integration of technologies such as artificial intelligence, big data analytics, and IoT is reshaping cities, making them more resilient, efficient, and sustainable. Digital transformation facilitates urban life while addressing environmental and economic challenges. Collaboration among stakeholders in IT, spatial planning, and sustainability is crucial for fostering innovation, ensuring inclusivity, and addressing urban challenges. In Slovakia, Metro'24 RockLine brought together policymakers, industry leaders, researchers, and urban planners to discuss opportunities and challenges in data-driven societies and smart infrastructure. The event served as a platform for knowledge exchange and strategic partnerships, emphasizing the importance of data-driven decision-making in urban development. Discussions focused on integrating emerging technologies, regulatory frameworks, and the socio-economic impacts of smart city initiatives. This qualitative study analyzed the event through content analysis of textual and audio-visual materials presented during the conference. Findings identified five dominant themes: cybersecurity, virtuality and energy ecosystems, artificial intelligence in tourism, intelligent buildings and districts, and electromobility. These topics reflect the increasing role of technology in shaping modern cities. Despite certain limitations, the study provides valuable insights for academia, industry, and the general public. It contributes to the growing body of research on smart cities, offering perspectives on how digital innovations can improve urban environments. Industry professionals can leverage these insights to refine technological applications, while policymakers can align regulations with emerging trends. Future research should explore the long-term impact of smart infrastructure, the scalability of solutions, and the socio-economic effects of digital integration. As cities continue evolving, the synergy of technology, governance, and sustainability will be essential for creating efficient, inclusive, and livable urban environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.242
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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