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Abstract Some performance-based research funding systems rely on expert-assigned journal rankings to allocate resources and guide research evaluation. In Finland, the JuFo system provides journal rankings, determined by experts who assess journals using available metadata, such as bibliometric indicators, alongside qualitative judgment. While prior work has explored machine learning approaches to approximate these rankings, the recent emergence of large language models (LLMs) offers new possibilities for automated, data-driven evaluation. In this study, we examine how well LLMs can replicate JuFo rankings when given the same structured information available to experts, including citation metrics, disciplinary assignments, and publisher metadata. We systematically compare LLM predictions to expert-assigned JuFo ranks using a confusion-matrix analysis to identify cases of alignment and deviation. Our research addresses two key questions: (1) how accurately LLMs estimate journal rankings, and (2) in which situations their predictions diverge from expert judgments and which factors explain these discrepancies. Our findings show that LLMs approximate expert-assigned rankings with high overall accuracy, with most errors occurring between adjacent levels. However, their performance varies systematically across disciplines, and they tend to under-predict top-tier journals, particularly in social sciences and humanities fields.

Abstract
Old-vine vineyards often contain dozens of grapevine varieties intermingled and irregularly distributed, making plant-level varietal identification slow and expensive when based on ampelography or molecular approaches. This paper proposes a field-oriented computer-vision pipeline for Vitis vinifera variety identification using images with a natural background from the historic Vinha Maria Teresa parcel (Quinta do Crasto, Portugal). A single-class YOLO11 detector is trained to localize the vine leaf and generate standardized crops, and a YOLO11 classifier is then fine-tuned on leaf regions of interest (ROIs) for eight selected varieties in the Douro UNESCO region. We annotated 2015 vineyard images for classification and supplemented detection training with 2648 additional leaf images; detectors (YOLO11n/s/m) were benchmarked under four augmentation regimes and evaluated on a fixed 48-image subset, including runtime on CPU and GPU. The best detector reached mAP@50-95 of 0.918 on the benchmark, while YOLO11n achieved similar to 27 FPS on CPU for fast cropping. On a 303-image test set, the best classifier (YOLO11s with mixed augmentations) achieved 94.06% Top-1 accuracy, 93.92% macro-F1, and 100% Top-5 accuracy with remaining errors concentrated among morphologically similar varieties. To assess deployment-oriented performance, classifiers trained under three input settings (manual crops, detector-generated crops, and full images) were evaluated on a held-out 48-image benchmark subset; removing the detection step reduced Top-1 accuracy from 75.00% to 68.75%, while the gap between manual and automatic crops was only 2.44 pp on successfully detected images with detection failures (14.6%) representing the primary operational bottleneck. Repeated retraining of the best manual-crop YOLO11s configuration across multiple random seeds showed stable performance with low variability in Top-1 accuracy and macro-F1. Under identical training conditions, ResNet50 and EfficientNet-B0 provided competitive baselines, but YOLO11s remained the strongest overall model on the held-out field benchmark. These results indicate that lightweight leaf detection plus crop-based classification can support scalable varietal identification in old vineyards under realistic acquisition conditions.

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Abstract
Abstract Misinformation poses one of the most urgent challenges of our society and raises the question of how to deal with it and manage its rapid spread. To address this problem, a promising approach relies on AI-based misinformation detection. This chapter of the book offers a critical analysis of the ethical implications associated with the design, deployment, and use of misinformation detectors (MDs). Designing and deploying an MD—an AI system that automatically identifies misinformation—is a complex undertaking that requires an interdisciplinary approach, as the challenges faced by MD designers and deployers encompass not only technical aspects, but also linguistic, sociological, political, and especially ethical dimensions. Our analysis is ethics-oriented and follows two main lines of inquiry: (1) Ethics by Design, which focuses on issues related to the design process of an MD, and (2) Ethics of Impact, which addresses the intended and unintended effects of MD deployment and use.

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Abstract
To fulfil their mission, museums strive to ensure a balance between the inclusive fruition of collections and preservation. In this sense, as part of their non-formal educational functions, some try to develop multimedia resources to explore content, engage, and promote knowledge. This paper is based on a gamified mobile application developed for a museum in Portugal, targeting young people between 8 and 12 years old, focusing on the evaluation phase of the experience’s impact. To this end, the Method for Assessing eXperience (MAX) was adopted. MAX is presented, and introduced changes are shared, in order to simplify its application. Using MAX’s simplified version, four sessions were established with a target group of 44 young museum visitors. Results show an overall satisfaction towards the gamified mobile application. It is possible to understand that 68% of the 44 young people considered the application very useful and 57% expressed the desire to use it again in the near future. Regarding emotions during the experience, a mere 2% reported confusion, while an overwhelming 98% showcased positive reactions. This study concludes that the application may have contributed positively to raising awareness for the preservation of cultural heritage and this assessment has enabled the overall improvement of the prototype. It is believed that the provided fun involvement might help to develop further knowledge regarding museum practices, supporting and enhancing info-communicational flows. © 2025 Elsevier B.V., All rights reserved.