{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YCGO4P2WI5LK7FMMKMV72QP6QQ","short_pith_number":"pith:YCGO4P2W","schema_version":"1.0","canonical_sha256":"c08cee3f564756af958c532bfd41fe8426a14273099b7dc9b08b4b37adc3c249","source":{"kind":"arxiv","id":"2501.14546","version":1},"attestation_state":"computed","paper":{"title":"Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hamid Sarmadi, Mattias Ohlsson, Ola Hall, Thorsteinn R\\\"ognvaldsson","submitted_at":"2025-01-24T14:49:00Z","abstract_excerpt":"This paper investigates the novel application of Large Language Models (LLMs) with vision capabilities to analyze satellite imagery for village-level poverty prediction. Although LLMs were originally designed for natural language understanding, their adaptability to multimodal tasks, including geospatial analysis, has opened new frontiers in data-driven research. By leveraging advancements in vision-enabled LLMs, we assess their ability to provide interpretable, scalable, and reliable insights into human poverty from satellite images. Using a pairwise comparison approach, we demonstrate that C"},"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":"2501.14546","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T14:49:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"78fe9f810256fbc279c38c9c4736e11644c9f4797747ece31ed910c09e8041ab","abstract_canon_sha256":"d95b5034419f9938f21ef14e1a939fcc31dd077917616decee185f557ec884f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:02.570790Z","signature_b64":"XROdyhlgRc7q8z/waKj6zTfM66FX40W/seHLW8QqY967KV/C4km6ZbgAxM9YnzQ1keN3Qs98WTFSIls1JApsCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c08cee3f564756af958c532bfd41fe8426a14273099b7dc9b08b4b37adc3c249","last_reissued_at":"2026-07-05T10:05:02.570375Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:02.570375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hamid Sarmadi, Mattias Ohlsson, Ola Hall, Thorsteinn R\\\"ognvaldsson","submitted_at":"2025-01-24T14:49:00Z","abstract_excerpt":"This paper investigates the novel application of Large Language Models (LLMs) with vision capabilities to analyze satellite imagery for village-level poverty prediction. Although LLMs were originally designed for natural language understanding, their adaptability to multimodal tasks, including geospatial analysis, has opened new frontiers in data-driven research. By leveraging advancements in vision-enabled LLMs, we assess their ability to provide interpretable, scalable, and reliable insights into human poverty from satellite images. Using a pairwise comparison approach, we demonstrate that C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14546","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/2501.14546/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":"2501.14546","created_at":"2026-07-05T10:05:02.570440+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.14546v1","created_at":"2026-07-05T10:05:02.570440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14546","created_at":"2026-07-05T10:05:02.570440+00:00"},{"alias_kind":"pith_short_12","alias_value":"YCGO4P2WI5LK","created_at":"2026-07-05T10:05:02.570440+00:00"},{"alias_kind":"pith_short_16","alias_value":"YCGO4P2WI5LK7FMM","created_at":"2026-07-05T10:05:02.570440+00:00"},{"alias_kind":"pith_short_8","alias_value":"YCGO4P2W","created_at":"2026-07-05T10:05:02.570440+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ","json":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ.json","graph_json":"https://pith.science/api/pith-number/YCGO4P2WI5LK7FMMKMV72QP6QQ/graph.json","events_json":"https://pith.science/api/pith-number/YCGO4P2WI5LK7FMMKMV72QP6QQ/events.json","paper":"https://pith.science/paper/YCGO4P2W"},"agent_actions":{"view_html":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ","download_json":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ.json","view_paper":"https://pith.science/paper/YCGO4P2W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.14546&json=true","fetch_graph":"https://pith.science/api/pith-number/YCGO4P2WI5LK7FMMKMV72QP6QQ/graph.json","fetch_events":"https://pith.science/api/pith-number/YCGO4P2WI5LK7FMMKMV72QP6QQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ/action/storage_attestation","attest_author":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ/action/author_attestation","sign_citation":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ/action/citation_signature","submit_replication":"https://pith.science/pith/YCGO4P2WI5LK7FMMKMV72QP6QQ/action/replication_record"}},"created_at":"2026-07-05T10:05:02.570440+00:00","updated_at":"2026-07-05T10:05:02.570440+00:00"}