This paper aims to test deep-learning-based Korean language models’ capacity to learn and detect social registers embedded in speech data, specifically age, gender, and regional dialects. A comprehensive understanding of linguistic phenomena requires contextualizing speech based on speakers’ age, gender, and geographic background, along with the processing of syntactic structures. To bridge the gap between human language understanding and model processing, we fine-tuned three representative Korean language models—KR-BERT, KoELECTRA-base, and KLUE-RoBERTa-base—using transcribed data from 4,000 hours of speech by middle-aged and elderly Korean speakers. The findings reveal that KoELECTRA-base outperformed the other two models across all social registers, which is likely attributed to its larger vocabulary and parameters size. Among the dialects, the Jeju dialect showed the highest accuracy in inference, which is attributed to its distinctiveness, making it easier for the models to detect. In addition to the fine-tuning process, we have made our fine-tuned models publicly available to support researchers interested in Korean computational sociolinguistics.

The objective of this study was to evaluate several types of uterine bacteria in Hanwoo. uterine bacteria from randomly selected 5 uterus was collected by flushing methods into a sterilized 1.5 ml centrifuge tube and was inoculated onto MacConkey agar and blood agar, respectively. After being incubated for 5% CO2, aerobic or anaerobic condition at 37℃ during 48h, bacterial colonies were selected and re-inoculated onto blood agar plates. Re-cultured colonies were identified by Gram staining and finally identified using Vitek system. The identified bacteria were Staphylococcus lentus, Staphylococcus sciuri, Staphylococcus vitulinus, Staphylococcus warneri of Gram (+) and Rhizobium radiobacter, Sphingomonas paucimobilis of Gram () bacteria. Although, pathogenicity of identified bacteria was unclear, the bacteria can have an effect on the uterine microenvironment. Therefore, repetitive research will be required to determine the effects of bacteria in cattle exposed to a various environment.