{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FFTAD66LRSYHGIW6SAUE2DJEBI","short_pith_number":"pith:FFTAD66L","canonical_record":{"source":{"id":"2501.00800","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2025-01-01T10:52:21Z","cross_cats_sorted":["math.DG"],"title_canon_sha256":"678aad5ddcf4c029c92456e34cd6b72b68ce760040dc13c7c74dce18e41d768e","abstract_canon_sha256":"922fe0ce1ff71d330e24cac1b496681969bc5cbce56de0d7289f934f6007ca1a"},"schema_version":"1.0"},"canonical_sha256":"296601fbcb8cb07322de90284d0d240a1ba014aa1547c0983b9212022c2bd5ba","source":{"kind":"arxiv","id":"2501.00800","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00800","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00800v1","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00800","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"pith_short_12","alias_value":"FFTAD66LRSYH","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"pith_short_16","alias_value":"FFTAD66LRSYHGIW6","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"pith_short_8","alias_value":"FFTAD66L","created_at":"2026-07-05T09:56:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FFTAD66LRSYHGIW6SAUE2DJEBI","target":"record","payload":{"canonical_record":{"source":{"id":"2501.00800","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2025-01-01T10:52:21Z","cross_cats_sorted":["math.DG"],"title_canon_sha256":"678aad5ddcf4c029c92456e34cd6b72b68ce760040dc13c7c74dce18e41d768e","abstract_canon_sha256":"922fe0ce1ff71d330e24cac1b496681969bc5cbce56de0d7289f934f6007ca1a"},"schema_version":"1.0"},"canonical_sha256":"296601fbcb8cb07322de90284d0d240a1ba014aa1547c0983b9212022c2bd5ba","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:10.424208Z","signature_b64":"nXyRJQMMep4N4+1rV5ELZxFmPtgVnx0yGwxedS9ipn/l2Ug1sQZM1k59WPTEHbQY4A/E2cpuLBGMvcZKWwwwCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"296601fbcb8cb07322de90284d0d240a1ba014aa1547c0983b9212022c2bd5ba","last_reissued_at":"2026-07-05T09:56:10.423785Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:10.423785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.00800","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:56:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YbdWYajOwMBgNBX3t68R/2Ly3/wAa30gxat+KUj3OIu+XdEuNWRKw8b8PhcOogKndxudWlIs5mpulZIsKhaqAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:18:20.793994Z"},"content_sha256":"7a57159752d91cdf889cad5c86b2217b8fb71997d31157caba4b345389ca2844","schema_version":"1.0","event_id":"sha256:7a57159752d91cdf889cad5c86b2217b8fb71997d31157caba4b345389ca2844"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FFTAD66LRSYHGIW6SAUE2DJEBI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Impact of Socio-Economic Challenges and Technological Progress on Economic Inequality: An Estimation with the Perelman Model and Ricci Flow Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.DG"],"primary_cat":"econ.EM","authors_text":"Davit Gondauri","submitted_at":"2025-01-01T10:52:21Z","abstract_excerpt":"The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The study aims to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00800","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/2501.00800/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:56:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VvnhxKNhcVtfrLmJd8pSb80prjYP3VjPnJBeVseUOymmvSTA6HGU/sHSpqHoVtsJUgpzZRxxnk7D+mrmxfEvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:18:20.796018Z"},"content_sha256":"b4ba7414689c49548eebdb97d48bb1a2d8a15e74b083365cac6586552c52d211","schema_version":"1.0","event_id":"sha256:b4ba7414689c49548eebdb97d48bb1a2d8a15e74b083365cac6586552c52d211"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FFTAD66LRSYHGIW6SAUE2DJEBI/bundle.json","state_url":"https://pith.science/pith/FFTAD66LRSYHGIW6SAUE2DJEBI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FFTAD66LRSYHGIW6SAUE2DJEBI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T18:18:20Z","links":{"resolver":"https://pith.science/pith/FFTAD66LRSYHGIW6SAUE2DJEBI","bundle":"https://pith.science/pith/FFTAD66LRSYHGIW6SAUE2DJEBI/bundle.json","state":"https://pith.science/pith/FFTAD66LRSYHGIW6SAUE2DJEBI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FFTAD66LRSYHGIW6SAUE2DJEBI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FFTAD66LRSYHGIW6SAUE2DJEBI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"922fe0ce1ff71d330e24cac1b496681969bc5cbce56de0d7289f934f6007ca1a","cross_cats_sorted":["math.DG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2025-01-01T10:52:21Z","title_canon_sha256":"678aad5ddcf4c029c92456e34cd6b72b68ce760040dc13c7c74dce18e41d768e"},"schema_version":"1.0","source":{"id":"2501.00800","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00800","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00800v1","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00800","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"pith_short_12","alias_value":"FFTAD66LRSYH","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"pith_short_16","alias_value":"FFTAD66LRSYHGIW6","created_at":"2026-07-05T09:56:10Z"},{"alias_kind":"pith_short_8","alias_value":"FFTAD66L","created_at":"2026-07-05T09:56:10Z"}],"graph_snapshots":[{"event_id":"sha256:b4ba7414689c49548eebdb97d48bb1a2d8a15e74b083365cac6586552c52d211","target":"graph","created_at":"2026-07-05T09:56:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.00800/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The study aims to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healt","authors_text":"Davit Gondauri","cross_cats":["math.DG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2025-01-01T10:52:21Z","title":"The Impact of Socio-Economic Challenges and Technological Progress on Economic Inequality: An Estimation with the Perelman Model and Ricci Flow Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00800","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7a57159752d91cdf889cad5c86b2217b8fb71997d31157caba4b345389ca2844","target":"record","created_at":"2026-07-05T09:56:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"922fe0ce1ff71d330e24cac1b496681969bc5cbce56de0d7289f934f6007ca1a","cross_cats_sorted":["math.DG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2025-01-01T10:52:21Z","title_canon_sha256":"678aad5ddcf4c029c92456e34cd6b72b68ce760040dc13c7c74dce18e41d768e"},"schema_version":"1.0","source":{"id":"2501.00800","kind":"arxiv","version":1}},"canonical_sha256":"296601fbcb8cb07322de90284d0d240a1ba014aa1547c0983b9212022c2bd5ba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"296601fbcb8cb07322de90284d0d240a1ba014aa1547c0983b9212022c2bd5ba","first_computed_at":"2026-07-05T09:56:10.423785Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:56:10.423785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nXyRJQMMep4N4+1rV5ELZxFmPtgVnx0yGwxedS9ipn/l2Ug1sQZM1k59WPTEHbQY4A/E2cpuLBGMvcZKWwwwCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:56:10.424208Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.00800","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7a57159752d91cdf889cad5c86b2217b8fb71997d31157caba4b345389ca2844","sha256:b4ba7414689c49548eebdb97d48bb1a2d8a15e74b083365cac6586552c52d211"],"state_sha256":"403fa682b3d4054588e7961f547454283964f4f7fabdfcc6649c56c8e1fa6fb0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mt/j4VqJI8R5NT1UJIrIDfHo9MsBEbUxh0HPvpTttchcvvcIbOvL+3s/W8xaEr/hLGb80n4HqEBjIbCkM0dwCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T18:18:20.802552Z","bundle_sha256":"90bcc3e99f8b3d22613721f71b9ae30021c4a8e09268278d680dc2262c1cf3ed"}}