@inproceedings{walkenhorst-mccormick-2025-scaling,
    title = "Scaling Species Diversity Analysis in Carbon Credit Projects with Large-Context {LLM}s",
    author = "Walkenhorst, Jessica  and
      McCormick, Colin",
    editor = "Dutia, Kalyan  and
      Henderson, Peter  and
      Leippold, Markus  and
      Manning, Christoper  and
      Morio, Gaku  and
      Muccione, Veruska  and
      Ni, Jingwei  and
      Schimanski, Tobias  and
      Stammbach, Dominik  and
      Singh, Alok  and
      Su, Alba (Ruiran)  and
      A. Vaghefi, Saeid",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.climatenlp-1.12/",
    doi = "10.18653/v1/2025.climatenlp-1.12",
    pages = "188--193",
    ISBN = "979-8-89176-259-6",
    abstract = "Reforestation and revegetation projects can help mitigate climate change because plant growth removes CO$_2$ from the air. However, the use of non-native species and monocultures in these projects may negatively affect biodiversity. Here, we describe a data pipeline to extract information about species that are planted or managed in over 1,000 afforestation/reforestation/revegetation and improved forest management projects, based on detailed project documentation. The pipeline leverages a large-context LLM and results in a macro-averaged recall of 79{\%} and a macro-averaged precision of 89{\%} across all projects and species."
}