{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:TM3ZYO6AJFMM7RZWHVIPY5O3S6","short_pith_number":"pith:TM3ZYO6A","schema_version":"1.0","canonical_sha256":"9b379c3bc04958cfc7363d50fc75db97b688472dcdf25c71e2c962681d9093a7","source":{"kind":"arxiv","id":"1702.01848","version":2},"attestation_state":"computed","paper":{"title":"Data-Driven Learning and Planning for Environmental Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Gaurav S. Sukhatme, Hordur K. Heidarsson, Kai-Chieh Ma, Lantao Liu","submitted_at":"2017-02-07T02:09:07Z","abstract_excerpt":"Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data is typically sparse and limited. The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper we pres"},"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":"1702.01848","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-02-07T02:09:07Z","cross_cats_sorted":[],"title_canon_sha256":"db8a9db352ab164daad6a42d64a3bad44721edcbc41840b188f863528bf13b1b","abstract_canon_sha256":"3b8e6187189e3c00dfb0f42873f8d427f4a157de90434435037f53a23d9ecee3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:59.223913Z","signature_b64":"H6ZwjA2fwsVXPqo9NJD4naLjtD8QxubBSXUxPyR9VEZb0i5SA3WrMCaSInEg+2smcj9b6EUEp2A3noYM+RFqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b379c3bc04958cfc7363d50fc75db97b688472dcdf25c71e2c962681d9093a7","last_reissued_at":"2026-05-18T00:19:59.223464Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:59.223464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-Driven Learning and Planning for Environmental Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Gaurav S. Sukhatme, Hordur K. Heidarsson, Kai-Chieh Ma, Lantao Liu","submitted_at":"2017-02-07T02:09:07Z","abstract_excerpt":"Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data is typically sparse and limited. The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper we pres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.01848","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":""},"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":"1702.01848","created_at":"2026-05-18T00:19:59.223530+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.01848v2","created_at":"2026-05-18T00:19:59.223530+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.01848","created_at":"2026-05-18T00:19:59.223530+00:00"},{"alias_kind":"pith_short_12","alias_value":"TM3ZYO6AJFMM","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"TM3ZYO6AJFMM7RZW","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"TM3ZYO6A","created_at":"2026-05-18T12:31:46.661854+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/TM3ZYO6AJFMM7RZWHVIPY5O3S6","json":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6.json","graph_json":"https://pith.science/api/pith-number/TM3ZYO6AJFMM7RZWHVIPY5O3S6/graph.json","events_json":"https://pith.science/api/pith-number/TM3ZYO6AJFMM7RZWHVIPY5O3S6/events.json","paper":"https://pith.science/paper/TM3ZYO6A"},"agent_actions":{"view_html":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6","download_json":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6.json","view_paper":"https://pith.science/paper/TM3ZYO6A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.01848&json=true","fetch_graph":"https://pith.science/api/pith-number/TM3ZYO6AJFMM7RZWHVIPY5O3S6/graph.json","fetch_events":"https://pith.science/api/pith-number/TM3ZYO6AJFMM7RZWHVIPY5O3S6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6/action/storage_attestation","attest_author":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6/action/author_attestation","sign_citation":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6/action/citation_signature","submit_replication":"https://pith.science/pith/TM3ZYO6AJFMM7RZWHVIPY5O3S6/action/replication_record"}},"created_at":"2026-05-18T00:19:59.223530+00:00","updated_at":"2026-05-18T00:19:59.223530+00:00"}