A Bayesian data-fusion model combines AI predictions and manual labels from camera traps to yield improved ecological inference and uncertainty quantification for white-tailed deer body condition.
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A multi-agent framework uses natural language to generate and execute Python code for dynamic bibliometric analysis including networks, clustering, and automated reports.
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Improving ecological inference and uncertainty quantification from camera trap data through the fusion of AI confidences and manual annotations
A Bayesian data-fusion model combines AI predictions and manual labels from camera traps to yield improved ecological inference and uncertainty quantification for white-tailed deer body condition.
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AI-Augmented Bibliometric Framework: A Paradigm Shift with Agentic AI for Dynamic, Snippet-Based Research Analysis
A multi-agent framework uses natural language to generate and execute Python code for dynamic bibliometric analysis including networks, clustering, and automated reports.