{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WOST6EHKE3SFVJ3Q6DRSOZM57F","short_pith_number":"pith:WOST6EHK","schema_version":"1.0","canonical_sha256":"b3a53f10ea26e45aa770f0e327659df9548740fadbd76db19ce8a42d4ef14069","source":{"kind":"arxiv","id":"2606.25584","version":1},"attestation_state":"computed","paper":{"title":"ML-MAWS: Alignment-Free Maximum Likelihood Phylogeny Estimation Using Minimal Absent Words","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.PE","authors_text":"Anonnya Sarkar and, Md. Manzurul Hasan, Papri Saha, Sudipta Kumar Das","submitted_at":"2026-06-24T08:56:31Z","abstract_excerpt":"Alignment-free methods in phylogenetic tree construction have major benefits in computational efficiency over alignment-based methods, but most sacrifice sequence information to pairwise distances, losing the statistical power of maximum likelihood (ML) inference. We describe ML-MAWS, an algorithm that fills this gap by encoding Minimal Absent Words (MAWs) as a binary presence/absence character matrix and estimating using an ML tree under the Lewis Mkv model using ascertainment bias correction. MAWs are obtained in linear time through the traversal of a suffix automaton. Three new elements con"},"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":"2606.25584","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.PE","submitted_at":"2026-06-24T08:56:31Z","cross_cats_sorted":[],"title_canon_sha256":"21f22b85ac20565a0aafb1ba726115410f674fc7c77cf7faae94ddba8075e011","abstract_canon_sha256":"ea7cffb3aac30123ed85874d34c538d59cdb212d767c890e5aa6ec55e6fc584d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:18:09.651356Z","signature_b64":"WPXopEvxENt5BH1LFm/FH6Nl/lV6HT7ZonJb7374F9w57dxkME4wz7FW00d9dX0xKFnulztciUVZrxkL490QCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3a53f10ea26e45aa770f0e327659df9548740fadbd76db19ce8a42d4ef14069","last_reissued_at":"2026-06-25T01:18:09.650792Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:18:09.650792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML-MAWS: Alignment-Free Maximum Likelihood Phylogeny Estimation Using Minimal Absent Words","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.PE","authors_text":"Anonnya Sarkar and, Md. Manzurul Hasan, Papri Saha, Sudipta Kumar Das","submitted_at":"2026-06-24T08:56:31Z","abstract_excerpt":"Alignment-free methods in phylogenetic tree construction have major benefits in computational efficiency over alignment-based methods, but most sacrifice sequence information to pairwise distances, losing the statistical power of maximum likelihood (ML) inference. We describe ML-MAWS, an algorithm that fills this gap by encoding Minimal Absent Words (MAWs) as a binary presence/absence character matrix and estimating using an ML tree under the Lewis Mkv model using ascertainment bias correction. MAWs are obtained in linear time through the traversal of a suffix automaton. Three new elements con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25584","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/2606.25584/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":"2606.25584","created_at":"2026-06-25T01:18:09.650885+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.25584v1","created_at":"2026-06-25T01:18:09.650885+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.25584","created_at":"2026-06-25T01:18:09.650885+00:00"},{"alias_kind":"pith_short_12","alias_value":"WOST6EHKE3SF","created_at":"2026-06-25T01:18:09.650885+00:00"},{"alias_kind":"pith_short_16","alias_value":"WOST6EHKE3SFVJ3Q","created_at":"2026-06-25T01:18:09.650885+00:00"},{"alias_kind":"pith_short_8","alias_value":"WOST6EHK","created_at":"2026-06-25T01:18:09.650885+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/WOST6EHKE3SFVJ3Q6DRSOZM57F","json":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F.json","graph_json":"https://pith.science/api/pith-number/WOST6EHKE3SFVJ3Q6DRSOZM57F/graph.json","events_json":"https://pith.science/api/pith-number/WOST6EHKE3SFVJ3Q6DRSOZM57F/events.json","paper":"https://pith.science/paper/WOST6EHK"},"agent_actions":{"view_html":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F","download_json":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F.json","view_paper":"https://pith.science/paper/WOST6EHK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.25584&json=true","fetch_graph":"https://pith.science/api/pith-number/WOST6EHKE3SFVJ3Q6DRSOZM57F/graph.json","fetch_events":"https://pith.science/api/pith-number/WOST6EHKE3SFVJ3Q6DRSOZM57F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F/action/storage_attestation","attest_author":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F/action/author_attestation","sign_citation":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F/action/citation_signature","submit_replication":"https://pith.science/pith/WOST6EHKE3SFVJ3Q6DRSOZM57F/action/replication_record"}},"created_at":"2026-06-25T01:18:09.650885+00:00","updated_at":"2026-06-25T01:18:09.650885+00:00"}