{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YRVNO7GXKCGK56TBNINCZXI3VC","short_pith_number":"pith:YRVNO7GX","schema_version":"1.0","canonical_sha256":"c46ad77cd7508caefa616a1a2cdd1ba897be41699566f64e007597996127a237","source":{"kind":"arxiv","id":"2412.12052","version":2},"attestation_state":"computed","paper":{"title":"Africanus I. Scalable, distributed and efficient radio data processing with Dask-MS and Codex Africanus","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Benjamin V. Hugo, Hertzog L. Bester, Jonathan S. Kenyon, Lexy A.L. Andati, Oleg M. Smirnov, Simon J. Perkins","submitted_at":"2024-12-16T18:21:42Z","abstract_excerpt":"New radio interferometers such as MeerKAT, SKA, ngVLA, and DSA-2000 drive advancements in software for two key reasons. First, handling the vast data from these instruments requires subdivision and multi-node processing. Second, their improved sensitivity, achieved through better engineering and larger data volumes, demands new techniques to fully exploit it. This creates a critical challenge in radio astronomy software: pipelines must be optimized to process data efficiently, but unforeseen artefacts from increased sensitivity require ongoing development of new techniques. This leads to a tra"},"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":"2412.12052","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2024-12-16T18:21:42Z","cross_cats_sorted":[],"title_canon_sha256":"e3a5c79d066573fef154cd374fccbbe0b0e7ba745fd7054b9a75b97b49708fe0","abstract_canon_sha256":"228c4e48f08dbf2ad1982605d6c5c32645b9ee39f0cae19322f095656b79a775"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:09.630464Z","signature_b64":"uahiYrl9VDiMeDjtXF5887RbZdc+G876/0mbS5hthI3mSHcZylYljeDOusGJA0D7TaYRGlKbew/CAnPpexYWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c46ad77cd7508caefa616a1a2cdd1ba897be41699566f64e007597996127a237","last_reissued_at":"2026-07-05T09:50:09.629901Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:09.629901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Africanus I. Scalable, distributed and efficient radio data processing with Dask-MS and Codex Africanus","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Benjamin V. Hugo, Hertzog L. Bester, Jonathan S. Kenyon, Lexy A.L. Andati, Oleg M. Smirnov, Simon J. Perkins","submitted_at":"2024-12-16T18:21:42Z","abstract_excerpt":"New radio interferometers such as MeerKAT, SKA, ngVLA, and DSA-2000 drive advancements in software for two key reasons. First, handling the vast data from these instruments requires subdivision and multi-node processing. Second, their improved sensitivity, achieved through better engineering and larger data volumes, demands new techniques to fully exploit it. This creates a critical challenge in radio astronomy software: pipelines must be optimized to process data efficiently, but unforeseen artefacts from increased sensitivity require ongoing development of new techniques. This leads to a tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12052","kind":"arxiv","version":2},"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/2412.12052/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":"2412.12052","created_at":"2026-07-05T09:50:09.629976+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.12052v2","created_at":"2026-07-05T09:50:09.629976+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12052","created_at":"2026-07-05T09:50:09.629976+00:00"},{"alias_kind":"pith_short_12","alias_value":"YRVNO7GXKCGK","created_at":"2026-07-05T09:50:09.629976+00:00"},{"alias_kind":"pith_short_16","alias_value":"YRVNO7GXKCGK56TB","created_at":"2026-07-05T09:50:09.629976+00:00"},{"alias_kind":"pith_short_8","alias_value":"YRVNO7GX","created_at":"2026-07-05T09:50:09.629976+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.10080","citing_title":"Africanus IV. The Stimela2 framework: scalable and reproducible workflows, from local to cloud compute","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC","json":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC.json","graph_json":"https://pith.science/api/pith-number/YRVNO7GXKCGK56TBNINCZXI3VC/graph.json","events_json":"https://pith.science/api/pith-number/YRVNO7GXKCGK56TBNINCZXI3VC/events.json","paper":"https://pith.science/paper/YRVNO7GX"},"agent_actions":{"view_html":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC","download_json":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC.json","view_paper":"https://pith.science/paper/YRVNO7GX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.12052&json=true","fetch_graph":"https://pith.science/api/pith-number/YRVNO7GXKCGK56TBNINCZXI3VC/graph.json","fetch_events":"https://pith.science/api/pith-number/YRVNO7GXKCGK56TBNINCZXI3VC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC/action/storage_attestation","attest_author":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC/action/author_attestation","sign_citation":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC/action/citation_signature","submit_replication":"https://pith.science/pith/YRVNO7GXKCGK56TBNINCZXI3VC/action/replication_record"}},"created_at":"2026-07-05T09:50:09.629976+00:00","updated_at":"2026-07-05T09:50:09.629976+00:00"}