ArticlePNAS nexus2024
Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- From electrocardiography to energetics: methods, constraints, and practical considerations for heart rate biologging in small animals.Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2026Review
- Convective rainfall drives behavioral shifts across multiple foraging scales in breeding seabirds.Movement ecology · 2026Article
- Six decades of animal accelerometry: trends, applications, and future directions.Movement ecology · 2026Review
- Review
- Real-Time Behaviour Recognition on Bio-Loggers Enables Autonomous Audio Playback Experiments in Free-Ranging Seabirds.Ecology and evolution · 2025Article
- The growing methodological toolkit for identifying and studying social learning and culture in non-human animals.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Mapping spatial memory in teleosts: a new Frontier in neural logging techniques.Frontiers in physiology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rare behaviors displayed by wild animals can generate new hypotheses; however, observing such behaviors may be challenging. While recent technological advancements, such as bio-loggers, may assist in documenting rare behaviors, the limited running time of battery-powered bio-loggers is insufficient to record rare behaviors when employing high-cost sensors (e.g. video cameras). In this study, we propose an artificial intelligence (AI)-enabled bio-logger that automatically detects outlier readings from always-on low-cost sensors, e.g. accelerometers, indicative of rare behaviors in target animals, without supervision by researchers, subsequently activating high-cost sensors to record only these behaviors. We implemented an on-board outlier detector via knowledge distillation by building a lightweight outlier classifier supervised by a high-cost outlier behavior detector trained in an unsupervised manner. The efficacy of AI bio-loggers has been demonstrated on seabirds, where videos and sensor data captured by the bio-loggers have enabled the identification of some rare behaviors, facilitating analyses of their frequency, and potential factors underlying these behaviors. This approach offers a means of documenting previously overlooked rare behaviors, augmenting our understanding of animal behavior.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.