Scientific research underpins effective conservation policy, yet current approaches for assessing whether scientific outputs meaningfully support defined management objectives rely primarily on manual expert review. This limitation constrains scalability, is time intensive and introduces potential bias in identifying knowledge gaps. We present a framework combining AI-assisted multi-target classification with systematic coverage analysis for automated evaluation of research alignment with conservation objectives. We compare traditional machine learning (TF-IDF + logistic regression), a generic BERT baseline, and an enhanced SciBERT approach incorporating domain-specific adaptations including multi-target architecture, balanced loss functions, and target weighting optimized for conservation science. The framework classifies research topics and conservation objective alignment, two dimensions requiring comprehension of scientific content and policy implications. We demonstrate the approach using 295 expert-annotated peer-reviewed studies from the Ross Sea region Marine Protected Area in Antarctica. Our enhanced multi-target SciBERT model achieved 70.0% macro F1, outperforming TF-IDF (59.5%) and BERT (52.0%) baselines, with per-target improvements of 21% on research topics and 14.5% on conservation objectives. The framework achieved 78% agreement with expert annotations, with particularly strong performance on conservation objective alignment (87.7% F1, 94% agreement). The integrated system successfully identified and quantified descriptive patterns in research coverage across thematic and policy dimensions, enabling systematic assessment for research prioritization and automated coverage analysis. While demonstrated in the Antarctic context, the framework architecture is broadly transferable, though successful adaptation requires retraining with domain-specific expert annotations and fine-tuning to match local management frameworks. • Enhanced multi-target SciBERT achieves 78% agreement with expert annotations for automated research-policy alignment assessment. • Framework reveals research coverage patterns with 59% of conservation topics appearing in <15 papers. • Multi-target architecture simultaneously classifies research themes and conservation objectives with 17.6% improvement over TF-IDF baseline. • Demonstrates scalable deployment across diverse contexts from Marine Protected Areas to SDG assessment. • Open-source implementation enables immediate adoption for evidence-based conservation planning.
McCarthy et al. (Sun,) studied this question.