{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YTPXSVFRTCIHIRA6EFRKWYEH5O","short_pith_number":"pith:YTPXSVFR","schema_version":"1.0","canonical_sha256":"c4df7954b1989074441e2162ab6087eba8da1c8252851d05664b880c458c28a3","source":{"kind":"arxiv","id":"2606.08428","version":1},"attestation_state":"computed","paper":{"title":"Optimal Harvesting under Stochastic Control: HJB Equation and Feynman-Kac Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Fatamatuj Johora, Paramahansa Pramanik","submitted_at":"2026-06-07T02:56:30Z","abstract_excerpt":"Sustainable resource management requires harvesting strategies that account for environmental variability and ecological uncertainty. This study investigates optimal harvesting of renewable biological resources within a stochastic framework, where population dynamics are influenced by random environmental fluctuations and modeled using stochastic differential equations. Two complementary approaches are employed: the Hamilton-Jacobi-Bellman (HJB) equation and the Feynman-Kac representation. The HJB framework provides a dynamic optimization rule and characterizes the value function through a non"},"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":"2606.08428","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-06-07T02:56:30Z","cross_cats_sorted":[],"title_canon_sha256":"2d1f9bb2ad48ccec11bfa4a7ec7f965e866e5bdcc6915a85da260853ba9eda04","abstract_canon_sha256":"66621551206bdc92e55557d1d04d8ac8ff4d18df837db7870b55d92bd0398f11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:36.341947Z","signature_b64":"XMjjEfJdZjofhYhfS5iHnhQUASZUWa7IPV5Wj0B4YarG+euEQF5rlY5opkqwo+XLfMiAnna6zR6PsL+SmkZVAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4df7954b1989074441e2162ab6087eba8da1c8252851d05664b880c458c28a3","last_reissued_at":"2026-06-09T01:05:36.341598Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:36.341598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Harvesting under Stochastic Control: HJB Equation and Feynman-Kac Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Fatamatuj Johora, Paramahansa Pramanik","submitted_at":"2026-06-07T02:56:30Z","abstract_excerpt":"Sustainable resource management requires harvesting strategies that account for environmental variability and ecological uncertainty. This study investigates optimal harvesting of renewable biological resources within a stochastic framework, where population dynamics are influenced by random environmental fluctuations and modeled using stochastic differential equations. Two complementary approaches are employed: the Hamilton-Jacobi-Bellman (HJB) equation and the Feynman-Kac representation. The HJB framework provides a dynamic optimization rule and characterizes the value function through a non"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08428","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/2606.08428/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":"2606.08428","created_at":"2026-06-09T01:05:36.341655+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.08428v1","created_at":"2026-06-09T01:05:36.341655+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08428","created_at":"2026-06-09T01:05:36.341655+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTPXSVFRTCIH","created_at":"2026-06-09T01:05:36.341655+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTPXSVFRTCIHIRA6","created_at":"2026-06-09T01:05:36.341655+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTPXSVFR","created_at":"2026-06-09T01:05:36.341655+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.27654","citing_title":"Modeling Educational Performance Using School Demographics and Teacher Characteristics","ref_index":245,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O","json":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O.json","graph_json":"https://pith.science/api/pith-number/YTPXSVFRTCIHIRA6EFRKWYEH5O/graph.json","events_json":"https://pith.science/api/pith-number/YTPXSVFRTCIHIRA6EFRKWYEH5O/events.json","paper":"https://pith.science/paper/YTPXSVFR"},"agent_actions":{"view_html":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O","download_json":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O.json","view_paper":"https://pith.science/paper/YTPXSVFR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.08428&json=true","fetch_graph":"https://pith.science/api/pith-number/YTPXSVFRTCIHIRA6EFRKWYEH5O/graph.json","fetch_events":"https://pith.science/api/pith-number/YTPXSVFRTCIHIRA6EFRKWYEH5O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O/action/storage_attestation","attest_author":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O/action/author_attestation","sign_citation":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O/action/citation_signature","submit_replication":"https://pith.science/pith/YTPXSVFRTCIHIRA6EFRKWYEH5O/action/replication_record"}},"created_at":"2026-06-09T01:05:36.341655+00:00","updated_at":"2026-06-09T01:05:36.341655+00:00"}