ClimateSense at ClimateCheck 2026

Thibault Ehrhart; Gregoire Burel; Raphael Troncy

doi:10.63317/36dhusfu9vmk

ClimateSense at ClimateCheck 2026

Thibault Ehrhart, Gregoire Burel, Raphael Troncy

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the external publication .)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

This paper describes our submission to the ClimateCheck 2026 shared task on scientific fact-checking of climate-related claims (Task 1) and disinformation narrative classification (Task 2). For Task 1, we use a three-stage pipeline combining BM25 retrieval over 394,269 scientific abstracts, ensemble re-ranking with five fine-tuned BGE cross-encoders aggregated via Reciprocal Rank Fusion, and zero-shot claim verification using the gpt-oss-120b model. For Task 2, we use a zero-shot approach with a custom prompt based on the CARDS taxonomy and the gpt 5.2 model. Our system outperforms the organizers’ baseline across all subtasks. Furthermore, we achieve the best result for the Task 1.1 (retrieval score of 0.466), and Task 1.2 (verification score of 1.183 - F1 + Recall@5), and the third best result for Task 2 (Macro F1 of 0.583).

Anthology ID:: 2026.nslp-1.7
Volume:: Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
Month:: May
Year:: 2026
Address:: Palma, Mallorca (Spain)
Editors:: Georg Rehm, Stefan Dietze, Danilo Dessi, Diana Maynard, Sonja Schimmler
Venues:: NSLP | WS
SIG:
Publisher:: ELRA Language Resources Association (ELRA)
Note:
Pages:: 66–77
Language:
External URL:: https://lrec.elra.info/lrec2026-ws-nslp-07
DOI:: 10.63317/36dhusfu9vmk
Bibkey:
Cite (ACL):: Thibault Ehrhart, Gregoire Burel, and Raphael Troncy. 2026. ClimateSense at ClimateCheck 2026. In Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026, pages 66–77, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).
Cite (Informal):: ClimateSense at ClimateCheck 2026 (Ehrhart et al., NSLP 2026)
Copy Citation:

External Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{ehrhart-etal-2026-climatesense,
    title = "{C}limate{S}ense at {C}limate{C}heck 2026",
    author = "Ehrhart, Thibault  and
      Burel, Gregoire  and
      Troncy, Raphael",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://aclanthology.org/2026.nslp-1.7/",
    doi = "10.63317/36dhusfu9vmk",
    pages = "66--77",
    abstract = "This paper describes our submission to the ClimateCheck 2026 shared task on scientific fact-checking of climate-related claims (Task 1) and disinformation narrative classification (Task 2). For Task 1, we use a three-stage pipeline combining BM25 retrieval over 394,269 scientific abstracts, ensemble re-ranking with five fine-tuned BGE cross-encoders aggregated via Reciprocal Rank Fusion, and zero-shot claim verification using the gpt-oss-120b model. For Task 2, we use a zero-shot approach with a custom prompt based on the CARDS taxonomy and the gpt 5.2 model. Our system outperforms the organizers' baseline across all subtasks. Furthermore, we achieve the best result for the Task 1.1 (retrieval score of 0.466), and Task 1.2 (verification score of 1.183 - F1 + Recall@5), and the third best result for Task 2 (Macro F1 of 0.583)."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ehrhart-etal-2026-climatesense">
    <titleInfo>
        <title>ClimateSense at ClimateCheck 2026</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Thibault</namePart>
        <namePart type="family">Ehrhart</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gregoire</namePart>
        <namePart type="family">Burel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Raphael</namePart>
        <namePart type="family">Troncy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Georg</namePart>
            <namePart type="family">Rehm</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Stefan</namePart>
            <namePart type="family">Dietze</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Danilo</namePart>
            <namePart type="family">Dessi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Diana</namePart>
            <namePart type="family">Maynard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sonja</namePart>
            <namePart type="family">Schimmler</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes our submission to the ClimateCheck 2026 shared task on scientific fact-checking of climate-related claims (Task 1) and disinformation narrative classification (Task 2). For Task 1, we use a three-stage pipeline combining BM25 retrieval over 394,269 scientific abstracts, ensemble re-ranking with five fine-tuned BGE cross-encoders aggregated via Reciprocal Rank Fusion, and zero-shot claim verification using the gpt-oss-120b model. For Task 2, we use a zero-shot approach with a custom prompt based on the CARDS taxonomy and the gpt 5.2 model. Our system outperforms the organizers’ baseline across all subtasks. Furthermore, we achieve the best result for the Task 1.1 (retrieval score of 0.466), and Task 1.2 (verification score of 1.183 - F1 + Recall@5), and the third best result for Task 2 (Macro F1 of 0.583).</abstract>
    <identifier type="citekey">ehrhart-etal-2026-climatesense</identifier>
    <identifier type="doi">10.63317/36dhusfu9vmk</identifier>
    <location>
        <url>https://aclanthology.org/2026.nslp-1.7/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>66</start>
            <end>77</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T ClimateSense at ClimateCheck 2026
%A Ehrhart, Thibault
%A Burel, Gregoire
%A Troncy, Raphael
%Y Rehm, Georg
%Y Dietze, Stefan
%Y Dessi, Danilo
%Y Maynard, Diana
%Y Schimmler, Sonja
%S Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F ehrhart-etal-2026-climatesense
%X This paper describes our submission to the ClimateCheck 2026 shared task on scientific fact-checking of climate-related claims (Task 1) and disinformation narrative classification (Task 2). For Task 1, we use a three-stage pipeline combining BM25 retrieval over 394,269 scientific abstracts, ensemble re-ranking with five fine-tuned BGE cross-encoders aggregated via Reciprocal Rank Fusion, and zero-shot claim verification using the gpt-oss-120b model. For Task 2, we use a zero-shot approach with a custom prompt based on the CARDS taxonomy and the gpt 5.2 model. Our system outperforms the organizers’ baseline across all subtasks. Furthermore, we achieve the best result for the Task 1.1 (retrieval score of 0.466), and Task 1.2 (verification score of 1.183 - F1 + Recall@5), and the third best result for Task 2 (Macro F1 of 0.583).
%R 10.63317/36dhusfu9vmk
%U https://aclanthology.org/2026.nslp-1.7/
%U https://doi.org/10.63317/36dhusfu9vmk
%P 66-77

Download as File

Markdown (Informal)

[ClimateSense at ClimateCheck 2026](https://aclanthology.org/2026.nslp-1.7/) (Ehrhart et al., NSLP 2026)

ClimateSense at ClimateCheck 2026 (Ehrhart et al., NSLP 2026)

ACL

Thibault Ehrhart, Gregoire Burel, and Raphael Troncy. 2026. ClimateSense at ClimateCheck 2026. In Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026, pages 66–77, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).