Abstract
As passive acoustic monitoring (PAM) datasets expand, studies increasingly use automated detectors to analyze baleen whale presence. However, detector errors like false positives (impacting precision, P; proportion of detected calls that are correct), and false negatives (impacting recall, R; proportion of available calls successfully detected) necessitate manual validation. Here, we demonstrate a tiered detection and validation approach to improve efficiency in sei whale ( Balaenoptera borealis ) call presence analysis that aims to maximize R while removing false positives. Using two detectors on eastern Canadian datasets, we tested multiple parameter settings, selecting one tier that captured only high‐quality calls (High‐P), one tier that captured most calls present but included more false positives (High‐R), and one tier that balanced the P and R values (Mid). To assess daily call presence, validation proceeded sequentially across tiers from High‐P to Mid to High‐R. For each subsequent tier, detections were only reviewed from days in which sei whale calls had not yet been confirmed. For both detectors, using this tiered approach reduced the overall manual validation effort (number of detections reviewed) when compared to validating the highest recall original outputs alone. This efficient strategy maintains accuracy in PAM studies and could be used for other species.