14 papers
PathoScribe is a unified retrieval-augmented large language model framework that transforms static pathology archives into an active, reasoning-enabled clinical intelligence platform, enabling natural language case retrieval, automated cohort construction, and real-time diagnostic support with high accuracy and efficiency.
This paper proposes Variable Substitution, a domain-specific graph augmentation technique that preserves the structural and semantic integrity of mathematical formulas, significantly enhancing the performance of graph contrastive learning models for mathematical information retrieval compared to generic strategies.
This paper presents a case study demonstrating how a Master of Social Work program utilized a locally deployed AI model to analyze over 40,000 job postings, generating actionable workforce intelligence on practice specializations and required competencies to inform strategic curriculum planning.
This study utilizes AI-assisted curation to compile and analyze a comprehensive database of 23,793 presentations from the Society for Social Work and Research Annual Conference (2005–2026), revealing significant growth in participation, collaboration, and international engagement alongside a continued predominance of quantitative research methods.
This paper presents a human-centred system design that leverages conversational AI and function-calling capabilities to enable natural language querying and visual-spatial exploration of nearly 1.7 million digitised natural history specimen records at the Australian Museum, overcoming the limitations of traditional keyword-based search tools.
This paper introduces a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND) and a machine-actionable taxonomy to enable ontology-aware multi-label classification and support the development of authority-grounded AI co-pilots for digital library subject indexing.
This paper proposes a structurally integrated framework for longitudinal science mapping that unifies thematic detection and lineage reconstruction within a single weighted relational architecture, replacing inconsistent set-theoretic overlap methods with a cohesive model of thematic evolution based on graded document affiliation and centrality-weighted structural relevance.
This doctoral work proposes a framework for fostering robust knowledge infrastructures in fragmented domains like science communication and aerospace engineering by implementing tool-supported workflows—including AI, knowledge graphs, and stakeholder-driven interfaces—while highlighting the need to address persistent societal and legal barriers to achieve full adoption.
This paper proposes using ChatGPT to perform sentiment analysis on citations within scientific articles, aiming to uncover nuanced reception patterns and detect potential biases or conflicts of interest to enhance the integrity of scholarly research.
This paper introduces Aerospace.Wikibase, a collaborative knowledge infrastructure built on Wikibase that addresses fragmentation in aerospace engineering by providing an open, extensible platform for sharing and curating over 700 terms related to processes, software, and data.
This paper introduces the SWARM-SLR AIssistant, a unified framework that enhances scalable Systematic Literature Review automation by integrating an LLM-guided agent, a modular tool registry, and persistent data storage to address previous usability and scalability challenges.
This scientometric analysis of Avian Influenza research from 2014 to 2023 reveals a steady global increase in publications led by Chinese and American institutions, highlighting key journals, collaborative networks, and the predominance of original articles to underscore the necessity for continued international cooperation.
An analysis of over 67,000 articles from 24 Canadian Science Publishing journals between 2010 and 2021 reveals that women remain underrepresented as authors, particularly in leadership roles and engineering fields, with disparities likely stemming from broader workforce inequalities rather than publication bias.
This paper presents a comprehensive, harmonized series of national and state-level datasets (2001–2025) detailing the structure, composition, and policy context of United States forensic DNA databases to facilitate longitudinal and cross-sectional analyses of their historical development and growth.