Title: Locating Rare Finds: Transductive Active Labeling Strategies in Ecological Research

Abstract: Active learning has become a routine method for annotating ecological data, allowing researchers to rapidly analyze extensive field datasets to monitor and comprehend natural ecosystems. However, the prevailing approach to assessing active learning is inductive, relying on predictive accuracy measured against a separate test set. We contend that this metric is ill-suited for the majority of ecological applications, which are fundamentally transductive in nature; their primary aim is to label an entire dataset pool with maximum efficiency. By overlooking the role of human interaction in the labeling process, current methods tend to undervalue the necessity of continued annotation, especially for long-tail classes that often hold significant ecological value, such as rare species or uncommon behaviors. Our findings indicate that for these rare categories, the transductive objective prioritizes discovery over prediction. The central difficulty lies in identifying "needles in the haystack"—instances of rare classes situated within dense clusters of common classes in latent geometry. We address this by introducing a new metric to quantify sampling difficulty. To bridge the gap between theory and practice, we suggest a conservative hybrid stopping rule derived from ecological rarefaction curves. Our results demonstrate that integrating discovery metrics with predictive performance prevents premature termination when dealing with long-tailed data, thereby enhancing the recovery of rare classes when the bottleneck is discovery rather than classification.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC