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    <abstract>Automatically identifying telescopes and their roles within astrophysical literature is crucial for large-scale scientific analysis and tracking instrument usage patterns. This paper describes the system developed by the “Clutch or Cry” team for the Telescope Reference and Astronomy Categorization Shared task (TRACS) at WASP 2025. The task involved two distinct challenges: multi-class telescope identification (Task 1) and multi-label role classification (Task 2). For Task 1, we employed a feature-centric approach combining document identifiers, metadata, and textual features to achieve high accuracy. For the more complex Task 2, we utilized a carefully designed two-level stacking ensemble. This hybrid model effectively fused symbolic information from a rule-based classifier with deep semantic understanding from a domain-adapted transformer. A subsequent meta-learning stage then performed targeted optimization for each role. These architectures were designed to address the primary challenges of handling long documents and managing severe class imbalance. A systematic optimization strategy focused on mitigating this imbalance significantly improved performance for minority classes. This work validates the effectiveness of using tailored, hybrid approaches and targeted optimization for complex classification tasks in specialized scientific domains.</abstract>
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%0 Conference Proceedings
%T “Clutch or Cry” Team at TRACS @ WASP2025: A Hybrid Stacking Ensemble for Astrophysical Document Classification
%A Khatib, Arshad
%A Prasad, Aayush
%A Trivedi, Rudra
%A Malviya, Shrikant
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%S Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India and virtual
%@ 979-8-89176-310-4
%F khatib-etal-2025-clutch
%X Automatically identifying telescopes and their roles within astrophysical literature is crucial for large-scale scientific analysis and tracking instrument usage patterns. This paper describes the system developed by the “Clutch or Cry” team for the Telescope Reference and Astronomy Categorization Shared task (TRACS) at WASP 2025. The task involved two distinct challenges: multi-class telescope identification (Task 1) and multi-label role classification (Task 2). For Task 1, we employed a feature-centric approach combining document identifiers, metadata, and textual features to achieve high accuracy. For the more complex Task 2, we utilized a carefully designed two-level stacking ensemble. This hybrid model effectively fused symbolic information from a rule-based classifier with deep semantic understanding from a domain-adapted transformer. A subsequent meta-learning stage then performed targeted optimization for each role. These architectures were designed to address the primary challenges of handling long documents and managing severe class imbalance. A systematic optimization strategy focused on mitigating this imbalance significantly improved performance for minority classes. This work validates the effectiveness of using tailored, hybrid approaches and targeted optimization for complex classification tasks in specialized scientific domains.
%U https://aclanthology.org/2025.wasp-main.17/
%P 146-156

Markdown (Informal)

[“Clutch or Cry” Team at TRACS @ WASP2025: A Hybrid Stacking Ensemble for Astrophysical Document Classification](https://aclanthology.org/2025.wasp-main.17/) (Khatib et al., WASP 2025)

• “Clutch or Cry” Team at TRACS @ WASP2025: A Hybrid Stacking Ensemble for Astrophysical Document Classification (Khatib et al., WASP 2025)

ACL

• Arshad Khatib, Aayush Prasad, Rudra Trivedi, and Shrikant Malviya. 2025. “Clutch or Cry” Team at TRACS @ WASP2025: A Hybrid Stacking Ensemble for Astrophysical Document Classification. In Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications, pages 146–156, Mumbai, India and virtual. Association for Computational Linguistics.