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Gertraud Medicus

Publications and source records attributed to Gertraud Medicus.

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Mechanical and Structural Contributions to Anisotropy in Granular Materials

Anisotropy in granular materials arises from both the internal fabric and the directionality of the stress state, yet separating these effects experimentally remains challenging. This study develops a first-order linearisation of the incremental stress-strain response that isolates mechanical anisotropy from structural anisotropy using two independent orientation measures. The formulation enables both contributions to be quantified directly from macroscopic laboratory data. The method is applied to hollow-cylinder tests with systematically varied loading directions. Results show that both anisotropy components intensify as the stress state becomes more deviatoric; mechanical anisotropy is consistently stronger; and its relative dominance decreases with increasing deviatoric stress. Comparison with an isotropic hypoplastic model confirms that mechanically induced directional effects are captured even without fabric anisotropy. The framework offers a practical and physically transparent means for quantifying and comparing anisotropy mechanisms in granular materials.

cond-mat.soft

Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results

The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establishing and strengthening stereotypes in society and also in the systems. To facilitate the studies of gender bias in the retrieval results of IR systems, we introduce Gender Representation-Bias for Information Retrieval (Grep-BiasIR), a novel thoroughly-audited dataset consisting of 118 bias-sensitive neutral search queries. The set of queries covers a wide range of gender-related topics, for which a biased representation of genders in the search result can be considered as socially problematic. Each query is accompanied with one relevant and one non-relevant document, where the document is also provided in three variations of female, male, and neutral. The dataset is available at https://github.com/KlaraKrieg/GrepBiasIR.

cs.IR