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At the present time, one of the most serious environmental problems of Central Asia and South Kazakhstan is the ongoing large-scale deterioration of principal urban tree populations. Several major centers of massive spread of invasive plant pests have been found in urban dendroflora of this region. The degree of damage of seven most wide-spread aboriginal tree species was found to range from 21.4±1.1 to 85.4±1.8%. In particular, the integrity of the native communities of sycamore (Platanus spp.), willow (Salix spp.), poplar (Populus spp.) and elm (Ulmus spp.) is highly endangered. Our taxonomic analysis of the most dangerous tree pests of the region has revealed them as neobiontic xylophilous insects such as Cossus cossus L. (Order: Lepidoptera L.) Monochamus urussovi Fisch., Monochamus sutor L., Acanthocinus aedelis L. and Ñetonia aureate L. (Order: Coleoptera L.). We relate the origin of this threatening trend with the import of industrial wood in the mid 90s of the last century that was associated with high degree of the constructional work in the region. Because of the absence of efficient natural predators of the pest species, the application of microbiological methods of the pest control and limitation is suggested.
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.