{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:25WP6U56SGEDFXNHBRO2HTRJX6","short_pith_number":"pith:25WP6U56","schema_version":"1.0","canonical_sha256":"d76cff53be918832dda70c5da3ce29bfa09411279222f19bb39a54357702b04b","source":{"kind":"arxiv","id":"1602.06466","version":1},"attestation_state":"computed","paper":{"title":"Analysis of animal accelerometer data using hidden Markov models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Megan Murgatroyd, Roland Langrock, Theoni Photopoulou, Toby A. Patterson, Vianey Leos-Barajas, Yannis P. Papastamatiou, Yuuki Watanabe","submitted_at":"2016-02-20T21:41:27Z","abstract_excerpt":"Use of accelerometers is now widespread within animal biotelemetry as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data there is a natural dependence between observations of movement or behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, either a supervised or an unsupervised learning approach can"},"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":"1602.06466","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2016-02-20T21:41:27Z","cross_cats_sorted":[],"title_canon_sha256":"4ba1617bbe9d2fb59070fdec690e17be7cb425f407550cee1360868cbbb1e144","abstract_canon_sha256":"80f37e2779d331615038c4d6d472f1c35cb37dc784f3dd3cceb21722f6453fde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:13.909611Z","signature_b64":"Uok1r4WWNWHvJW1PRTE26tHuHAX7d+qXxjvy/czzq1xj6wZKRqPdKfk0UUQIgm32k6THVeAyTGki/Uk1fBcRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d76cff53be918832dda70c5da3ce29bfa09411279222f19bb39a54357702b04b","last_reissued_at":"2026-05-18T01:20:13.909051Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:13.909051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analysis of animal accelerometer data using hidden Markov models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Megan Murgatroyd, Roland Langrock, Theoni Photopoulou, Toby A. Patterson, Vianey Leos-Barajas, Yannis P. Papastamatiou, Yuuki Watanabe","submitted_at":"2016-02-20T21:41:27Z","abstract_excerpt":"Use of accelerometers is now widespread within animal biotelemetry as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data there is a natural dependence between observations of movement or behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, either a supervised or an unsupervised learning approach can"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.06466","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":""},"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":"1602.06466","created_at":"2026-05-18T01:20:13.909128+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.06466v1","created_at":"2026-05-18T01:20:13.909128+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.06466","created_at":"2026-05-18T01:20:13.909128+00:00"},{"alias_kind":"pith_short_12","alias_value":"25WP6U56SGED","created_at":"2026-05-18T12:29:52.810259+00:00"},{"alias_kind":"pith_short_16","alias_value":"25WP6U56SGEDFXNH","created_at":"2026-05-18T12:29:52.810259+00:00"},{"alias_kind":"pith_short_8","alias_value":"25WP6U56","created_at":"2026-05-18T12:29:52.810259+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/25WP6U56SGEDFXNHBRO2HTRJX6","json":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6.json","graph_json":"https://pith.science/api/pith-number/25WP6U56SGEDFXNHBRO2HTRJX6/graph.json","events_json":"https://pith.science/api/pith-number/25WP6U56SGEDFXNHBRO2HTRJX6/events.json","paper":"https://pith.science/paper/25WP6U56"},"agent_actions":{"view_html":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6","download_json":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6.json","view_paper":"https://pith.science/paper/25WP6U56","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.06466&json=true","fetch_graph":"https://pith.science/api/pith-number/25WP6U56SGEDFXNHBRO2HTRJX6/graph.json","fetch_events":"https://pith.science/api/pith-number/25WP6U56SGEDFXNHBRO2HTRJX6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6/action/storage_attestation","attest_author":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6/action/author_attestation","sign_citation":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6/action/citation_signature","submit_replication":"https://pith.science/pith/25WP6U56SGEDFXNHBRO2HTRJX6/action/replication_record"}},"created_at":"2026-05-18T01:20:13.909128+00:00","updated_at":"2026-05-18T01:20:13.909128+00:00"}