# amc: The Automated Mission Classifier for Telescope Bibliographies

arXiv: https://arxiv.org/abs/2512.11202 · HTML: https://mlsanity.com/paper/2512.11202/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://mlsanity.com/paper/2212.14024/) (2022)
- [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://mlsanity.com/paper/2406.11695/) (2024)
- [AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy](https://mlsanity.com/paper/2505.20538/) (2025)
- [Delving into the Utilisation of ChatGPT in Scientific Publications in Astronomy](https://mlsanity.com/paper/2406.17324/) (2024)
- [SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMs](https://mlsanity.com/paper/2508.08742/) (2025)
- [What is the Role of Large Language Models in the Evolution of Astronomy Research?](https://mlsanity.com/paper/2409.20252/) (2024)
- [Large Language Models Achieve Gold Medal Performance at the International Olympiad on Astronomy & Astrophysics (IOAA)](https://mlsanity.com/paper/2510.05016/) (2025)
- [From Queries to Criteria: Understanding How Astronomers Evaluate LLMs](https://mlsanity.com/paper/2507.15715/) (2025)
- [Robust Archives Maximize Scientific Accessibility](https://mlsanity.com/paper/1907.06234/) (2019)
- [Assessing your Observatory's Impact: Best Practices in Establishing and Maintaining Observatory Bibliographies](https://mlsanity.com/paper/2401.00060/) (2023)
- [Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories](https://mlsanity.com/paper/2411.00987/) (2024)

## Abstract (arXiv)

Telescope bibliographies record the pulse of astronomy research by capturing publication statistics and citation metrics for telescope facilities. Robust and scalable bibliographies ensure that we can measure the scientific impact of our facilities and archives. However, the growing rate of publications threatens to outpace our ability to manually label astronomical literature. We therefore present the Automated Mission Classifier (amc), a tool that uses large language models (LLMs) to identify and categorize telescope references by processing large quantities of paper text. A modified version of amc performs well on the TRACS Kaggle challenge, achieving a macro F1 score of 0.84 on the held-out test set. amc is valuable for other telescopes beyond TRACS; we developed the initial software for identifying papers that featured scientific results by NASA missions. Additionally, we investigate how amc can also be used to interrogate historical datasets and surface potential label errors. Our work demonstrates that LLM-based applications offer powerful and scalable assistance for library sciences.