CURRENT PROJECTS
Investigating the Link Between Personality and Problem-Solving in Captive Gray Wolves
Graduate student: Yasmeen Ghavamian
Date: 2022-present
Goals: To evaluate the personality of captive gray wolves living in packs and assess individual differences in problem-solving strategies to understand how an individual’s personality may predict their ability to cope with changes in their environment.
Personality Traits in Beef Cattle and how they Relate to Grazing Distribution on Extensive Rangelands
Graduate Student: Maggie Creamer
Date: 2019 – Present
Goals:
- Identify reliable multidimensional personality traits in beef cattle across several contexts
- Relate personality measures to foraging patterns and movement via GPS data
Investigation of Consistent Individual Differences During Pre- and Post-Natal, Human-Animal Interactions in Rangeland Ewes.
Graduate student: Kaleiah Schiller
Date: 2019 – present
Goals: Assess consistency of behavioral responses towards a human stimulus during and outside of the lambing season to understand context specificity or domain generality of phenotypic traits in rangeland ewes.
Publications:
PAST PROJECTS
Livestock Informatics Toolkit (LIT)
Graduate Student: Catie McVey
Date: 2019 – 2022
Goals:
- Developing Unsupervised Machine Learning (UML) algorithms to visualize and quantify complex behavioral patterns.
- Using Information Theoretic Approaches to reveal the complex relationships between performance on controlled behavioral assays, home pen behaviors recorded by precision manage-met tools, and outcome measures such as health and productivity.
Open Source Code:
https://cgmcvey.github.io/LIT/#references
Publications:
McVey C, Hsieh F, Manriquez D, Pinedo P and Horback K (2020) Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques. Front. Vet. Sci. 7:523. doi: 10.3389/fvets.2020.00523
McVey, C., Hsieh, F., Manriquez, D., Pinedo, P., & Horback, K. (2022). Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches. Sensors, 22(1), 1.
Facial Inference Toolkit (FIT)
Graduate Student: Catie McVey
Date: 2019 – 2022
Goals:
- Develop algorithmic tools (projective biometrics) to quantify facial structure in livestock
- Developing algorithmic tools to correct for the impact of camera angle on measurements from 2D images of minimally restrained livestock
- Developing algorithmic tools to quantify facial expressions in livestocK
Effect of rearing environment on the development of depth perception in egg-laying hens
Graduate Student: Claire Jones
Date: 2019 – 2021
Goals: Assess depth perception in egg laying hens using a Y-maze task and visual cliff apparatus.
Results here: Jones NA ISAE poster 2022