Jarred W. Yates
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Also affiliated: West Virginia University (2020–2026); West Virginia University Hospitals (2025)
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Jarred W. Yates' research focuses on developing and applying machine learning techniques to animal agriculture, particularly for predicting nutrient intake and evaluating stockmanship. His recent work has utilized gradient boosting regression, Gaussian process boosting regression, and long short-term memory models to predict dry matter intake (DMI) and water intake in beef cattle. These models are designed for scalability and deployability, incorporating elements of MLOps and containerization. Yates has also contributed to the development and validation of quantitative tools for assessing stockmanship in beef cattle, such as the "Stockman’s Scorecard." Additionally, his research includes developing frameworks for managing organic waste in rural agricultural regions, aiming for sustainable practices. He has published on novel methods for measuring water intake in cattle and its application to DMI determination. Yates' work integrates computational methods with animal science to improve management and resource utilization in agriculture.
Metrics
- h-index: 4
- Publications: 18
- Citations: 28
Selected Publications
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Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates (2026)
Collaboration Network
Top Collaborators
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
- Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates
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