{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2PAYR7LPLRRQCTFHGIXKBP3OWC","short_pith_number":"pith:2PAYR7LP","schema_version":"1.0","canonical_sha256":"d3c188fd6f5c63014ca7322ea0bf6eb09a3672c66d2b635a6ac53424650a5f5b","source":{"kind":"arxiv","id":"2506.13796","version":1},"attestation_state":"computed","paper":{"title":"ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ming Lin, Xiao Wang, Yuanhong Liao, Yuqi Bai, Zhou Chen","submitted_at":"2025-06-12T08:43:38Z","abstract_excerpt":"As the issue of global climate change becomes increasingly severe, the demand for research in climate science continues to grow. Natural language processing technologies, represented by Large Language Models (LLMs), have been widely applied to climate change-specific research, providing essential information support for decision-makers and the public. Some studies have improved model performance on relevant tasks by constructing climate change-related instruction data and instruction-tuning LLMs. However, current research remains inadequate in efficiently producing large volumes of high-precis"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.13796","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-12T08:43:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7ec8d92d6ffc50ca1a79785d4b5b700ff7245b6f7c6ae0767afb25e2183a13fa","abstract_canon_sha256":"be459e21caf08809a3e3d812bd8d8e664684e42efe1e3ee359ada9b35e0cc36b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:43.549747Z","signature_b64":"ZlGJiqDmX4PwXjXeK7oc8q2C2rYHFC2q2cOLXNCJKb6weNM5+HgYOEDTZjblpA8xR1jdx6dG6TXB+AOpd57NDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3c188fd6f5c63014ca7322ea0bf6eb09a3672c66d2b635a6ac53424650a5f5b","last_reissued_at":"2026-07-05T11:22:43.549262Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:43.549262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ming Lin, Xiao Wang, Yuanhong Liao, Yuqi Bai, Zhou Chen","submitted_at":"2025-06-12T08:43:38Z","abstract_excerpt":"As the issue of global climate change becomes increasingly severe, the demand for research in climate science continues to grow. Natural language processing technologies, represented by Large Language Models (LLMs), have been widely applied to climate change-specific research, providing essential information support for decision-makers and the public. Some studies have improved model performance on relevant tasks by constructing climate change-related instruction data and instruction-tuning LLMs. However, current research remains inadequate in efficiently producing large volumes of high-precis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13796","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.13796/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.13796","created_at":"2026-07-05T11:22:43.549336+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.13796v1","created_at":"2026-07-05T11:22:43.549336+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13796","created_at":"2026-07-05T11:22:43.549336+00:00"},{"alias_kind":"pith_short_12","alias_value":"2PAYR7LPLRRQ","created_at":"2026-07-05T11:22:43.549336+00:00"},{"alias_kind":"pith_short_16","alias_value":"2PAYR7LPLRRQCTFH","created_at":"2026-07-05T11:22:43.549336+00:00"},{"alias_kind":"pith_short_8","alias_value":"2PAYR7LP","created_at":"2026-07-05T11:22:43.549336+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC","json":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC.json","graph_json":"https://pith.science/api/pith-number/2PAYR7LPLRRQCTFHGIXKBP3OWC/graph.json","events_json":"https://pith.science/api/pith-number/2PAYR7LPLRRQCTFHGIXKBP3OWC/events.json","paper":"https://pith.science/paper/2PAYR7LP"},"agent_actions":{"view_html":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC","download_json":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC.json","view_paper":"https://pith.science/paper/2PAYR7LP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.13796&json=true","fetch_graph":"https://pith.science/api/pith-number/2PAYR7LPLRRQCTFHGIXKBP3OWC/graph.json","fetch_events":"https://pith.science/api/pith-number/2PAYR7LPLRRQCTFHGIXKBP3OWC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC/action/storage_attestation","attest_author":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC/action/author_attestation","sign_citation":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC/action/citation_signature","submit_replication":"https://pith.science/pith/2PAYR7LPLRRQCTFHGIXKBP3OWC/action/replication_record"}},"created_at":"2026-07-05T11:22:43.549336+00:00","updated_at":"2026-07-05T11:22:43.549336+00:00"}