Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse

Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse

Autonomous recording units are increasingly being used to monitor wildlife on large geographic and temporal scales, paired with machine learning (ML) to automate detection of wildlife. However, false positive detections from ML classifiers can result in erroneous ecological models that can lead to m...

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Bibliographic Details
Main Authors: Laurence A. Clarfeld, Katherina D. Gieder, Robert Abrams, Christopher Bernier, Joseph Cahill, Susan Staats, Scott Wixsom, Therese M. Donovan
Format: Article
Language:English
Published: Elsevier 2025-11-01
Series:Ecological Informatics
Subjects:
Acoustic monitoring
Artificial intelligence
Bioacoustics
Machine learning
Wildlife monitoring
Online Access:http://www.sciencedirect.com/science/article/pii/S157495412500175X
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