Abstract
Bees play a critical role as pollinators in ecosystem services, contributing significantly to the sexual reproduction and diversity of plants. The Caatinga biome in Brazil, home to around 200 bee species, provides an ideal habitat for these species due to its unique climate conditions. However, this biome faces threats from anthropogenic processes, making it urgent to characterise the local bee populations efficiently. Traditional taxonomic surveys for bee identification are complex due to the lack of suitable keys and expertise required. As a result, molecular barcoding has emerged as a valuable tool, using genome regions to compare and identify bee species. However, little is known about Caatinga bees to develop these molecular tools further. This study addresses this gap, providing an updated list of 262 Caatinga bee species across 86 genera and identifying similar to 40 primer sets to aid in barcoding these species. The findings highlight the ongoing work needed to fully characterise the Caatinga biome's bee distribution and species or subspecies to support more effective monitoring and conservation efforts.

Abstract
The importance of time series forecasting drives continuous research and the development of new approaches to tackle this problem. Typically, these methods are introduced through empirical studies that frequently claim superior accuracy for the proposed approaches. Nevertheless, concerns are rising about the reliability and generalizability of these results due to limitations in experimental setups. This paper addresses a critical limitation: the number and representativeness of the datasets used. We investigate the impact of dataset selection bias, particularly the practice of cherry-picking datasets, on the performance evaluation of forecasting methods. Through empirical analysis with a diverse set of benchmark datasets, our findings reveal that cherry-picking datasets can significantly distort the perceived performance of methods, often exaggerating their effectiveness. Furthermore, our results demonstrate that by selectively choosing just four datasets - what most studies report - 46% of methods could be deemed best in class, and 77% could rank within the top three. Additionally, recent deep learning-based approaches show high sensitivity to dataset selection, whereas classical methods exhibit greater robustness. Finally, our results indicate that, when empirically validating forecasting algorithms on a subset of the benchmarks, increasing the number of datasets tested from 3 to 6 reduces the risk of incorrectly identifying an algorithm as the best one by approximately 40%. Our study highlights the critical need for comprehensive evaluation frameworks that more accurately reflect real-world scenarios. Adopting such frameworks will ensure the development of robust and reliable forecasting methods.

Abstract
Purpose – This study aims to comparatively evaluate two semantic models, ArchOnto (CIDOC CRM based) and Records in Contexts Ontology (RiC-O), for archival representation within the Linked Open Data framework. The research seeks to critically analyse their ability to represent archival documents, events, activities, and provenance through the application on a case study of historical baptism records. Design/methodology/approach – The study adopted a comparative approach, utilising the two models to represent a dataset of baptism records from a Portuguese parish spanning several centuries. This involved information extraction and conversion processes, transforming XML EAD finding aids into RDF to facilitate more explicit semantic representation and analysis. Findings – The analysis revealed distinctive strengths and limitations of each semantic model, providing nuanced insights into their respective capacities for archival description. The findings guide cultural heritage institutions in selecting and implementing the most suitable semantic model for their needs and pave the way for semantic alignment between the two models. Research limitations/implications – Although the case study explored the representation of a wide range of features, potential limitations include the specific contextual constraints of parish records and the need for broader comparative studies across diverse archival contexts. Originality/value – This paper offers original insights into semantic modelling for archival representations by providing a detailed comparative analysis of two ontological approaches. It offers valuable perspectives for archivists, digital humanities researchers, and cultural heritage professionals seeking to enhance the semantic richness of archival descriptions. © 2025 Emerald Publishing Limited

Abstract
We present an adequacy theorem for a concurrent extension of probabilistic GCL. The underlying denotational semantics is based on the so-called mixed powerdomains, which combine non-determinism with probabilistic behaviour. The theorem itself is formulated via M. Smyth's idea of treating observable properties as open sets of a topological space. The proof hinges on a 'topological generalisation' of Konig's lemma in the setting of probabilistic programming (a result that is proved in the paper as well). One application of the theorem is that it entails semi-decidability w.r.t. whether a concurrent program satisfies an observable property (written in a certain form). This is related to M. Escardo's conjecture about semi-decidability w.r.t. may and must probabilistic testing.

Abstract
The authors wish to make the following corrections to this manuscript [...]

Abstract