{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6NT5HTPQP2SEOAD6KYQIMC63MW","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":"0325f65c316fd2b4089c8176b84cef97f13e53df13e748442162f8b909f0f57d","cross_cats_sorted":["cs.SY","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-08-30T21:08:20Z","title_canon_sha256":"116474742a5b1379195cd7ebfda0c26aa78376624d4d5d82aef3153688d634aa"},"schema_version":"1.0","source":{"id":"2509.00608","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.00608","created_at":"2026-05-29T02:05:36Z"},{"alias_kind":"arxiv_version","alias_value":"2509.00608v3","created_at":"2026-05-29T02:05:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00608","created_at":"2026-05-29T02:05:36Z"},{"alias_kind":"pith_short_12","alias_value":"6NT5HTPQP2SE","created_at":"2026-05-29T02:05:36Z"},{"alias_kind":"pith_short_16","alias_value":"6NT5HTPQP2SEOAD6","created_at":"2026-05-29T02:05:36Z"},{"alias_kind":"pith_short_8","alias_value":"6NT5HTPQ","created_at":"2026-05-29T02:05:36Z"}],"graph_snapshots":[{"event_id":"sha256:90b1db742abaebfd1a2691cab514fe113c6a78fc76673384c6b34e274aedee79","target":"graph","created_at":"2026-05-29T02:05:36Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Field tests demonstrate a collar recognition F1 score of 98.6% at a throughput of 1000 Sa/s. Notably, the algorithm requires only 1.5 μs per sample, confirming its computational efficiency and suitability for deployment on resource-constrained, high-temperature downhole platforms."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The physical plausibility verification rules remain effective and general across varying well conditions, interference levels, and casing configurations beyond those encountered in the reported field tests."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"The DTPPMP system achieves 98.6% F1 score in collar recognition for self-locating perforating at 1000 Sa/s using dynamic threshold and physical plausibility algorithms that run in 1.5 μs per sample."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Dynamic threshold and physical plausibility checks enable real-time collar recognition for automated well perforating."}],"snapshot_sha256":"885ac9adea254e40863d9ce91896ecc10651d77367601b0e5956dc71dc39b307"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"ebdc36805467bee1900121be12d4dc3f32144e8e7e565825ab564fb81c0be188"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.00608/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate depth measurement is critical for targeting designated perforation intervals to maximize hydrocarbon recovery. While next-generation automated wireless perforating techniques reduce reliance on costly surface infrastructure and personnel, they lack the continuous depth correlation provided by conventional wireline cables. Consequently, correlating real-time casing collar locator (CCL) signals with a pre-recorded casing tally is essential for automatic depth determination. However, implementing this measurement remains challenging: downhole instruments must process CCL signals in real-","authors_text":"Guo-Hui Ren, Jun-Jie Wang, Kai Tang, Shuang Liu, Si-Yu Xiao, Tian-Hao Mao, Tu-Pei Chen, Xin-Di Zhao, Yang Liu, Yi-An Liu, Yu-Qiao Chen, Zhi-Jian Yu","cross_cats":["cs.SY","eess.SP"],"headline":"Dynamic threshold and physical plausibility checks enable real-time collar recognition for automated well perforating.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-08-30T21:08:20Z","title":"Realization of Precise Perforating Using Dynamic Threshold and Physical Plausibility Algorithm for Self-Locating Perforating in Oil and Gas Wells"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00608","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-18T19:12:29.427139Z","id":"62787cdd-e22b-4b0e-b8dc-f7ead9d75dc0","model_set":{"reader":"grok-4.3"},"one_line_summary":"The DTPPMP system achieves 98.6% F1 score in collar recognition for self-locating perforating at 1000 Sa/s using dynamic threshold and physical plausibility algorithms that run in 1.5 μs per sample.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Dynamic threshold and physical plausibility checks enable real-time collar recognition for automated well perforating.","strongest_claim":"Field tests demonstrate a collar recognition F1 score of 98.6% at a throughput of 1000 Sa/s. Notably, the algorithm requires only 1.5 μs per sample, confirming its computational efficiency and suitability for deployment on resource-constrained, high-temperature downhole platforms.","weakest_assumption":"The physical plausibility verification rules remain effective and general across varying well conditions, interference levels, and casing configurations beyond those encountered in the reported field tests."}},"verdict_id":"62787cdd-e22b-4b0e-b8dc-f7ead9d75dc0"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:08c4b6005014af7c78cf368330701eaf9312e33eec1ed986dafb0cd5e0120b7f","target":"record","created_at":"2026-05-29T02:05:36Z","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":"0325f65c316fd2b4089c8176b84cef97f13e53df13e748442162f8b909f0f57d","cross_cats_sorted":["cs.SY","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-08-30T21:08:20Z","title_canon_sha256":"116474742a5b1379195cd7ebfda0c26aa78376624d4d5d82aef3153688d634aa"},"schema_version":"1.0","source":{"id":"2509.00608","kind":"arxiv","version":3}},"canonical_sha256":"f367d3cdf07ea447007e5620860bdb65a2bd4b9a5ec7c5c74afdc9e9b20212fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f367d3cdf07ea447007e5620860bdb65a2bd4b9a5ec7c5c74afdc9e9b20212fe","first_computed_at":"2026-05-29T02:05:36.067432Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T02:05:36.067432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1Ht+Hq+LekCe5Ut5BIMBFb8kT0H/kasgrPRs3VKKcXZu15JAvBP624M09tM2CUeiBwSissBsWXP7nrkkHPhhBg==","signature_status":"signed_v1","signed_at":"2026-05-29T02:05:36.068169Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.00608","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08c4b6005014af7c78cf368330701eaf9312e33eec1ed986dafb0cd5e0120b7f","sha256:90b1db742abaebfd1a2691cab514fe113c6a78fc76673384c6b34e274aedee79"],"state_sha256":"0134ed99e119875ec412d1ca3eeb66df2bde69b2fa59a6db85f452eff1e37c24"}