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**By Sophia Mangrubang**
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Behavioral observation and data collection is an integral part of maintaining animals in captivity. Large corporations, such as zoos, rely on staff members to observe animal behaviors as a means of assessing their health. However, without constant surveillance, key behavioral patterns can be overlooked. Machine learning provides a unique opportunity to reduce the time-commitment of staff, increase monitoring time, and produce visualizations that provide valuable insights.
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Currently, there are a few major products that utilize machine learning methods for behavioral observation in captivity. These models are insightful, but lack the accessibility and customization of an open source model.
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# Behavioral Tracking Model for Captive Animals
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**By Sophia Mangrubang**
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## Scientific Context
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Behavioral observation and data collection is an integral part of maintaining animals in captivity. Large corporations, such as zoos, rely on staff members to observe animal behaviors as a means of assessing their health. However, without constant surveillance, key behavioral patterns can be overlooked. Machine learning provides a unique opportunity to reduce the time-commitment of staff, increase monitoring time, and produce visualizations that provide valuable insights.
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Currently, there are a few major products that utilize machine learning methods for behavioral observation in captivity. These models are insightful, but lack the accessibility and customization of an open source model.
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