{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3JDH2VHPUDHEF4OQ3KK3PQJIR5","short_pith_number":"pith:3JDH2VHP","schema_version":"1.0","canonical_sha256":"da467d54efa0ce42f1d0da95b7c1288f4c7e03be6523bb3809309044112eaa33","source":{"kind":"arxiv","id":"2505.10770","version":1},"attestation_state":"computed","paper":{"title":"Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.RO","authors_text":"Ebasa Temesgen, Graham Wilson, Greta Brown, Maria Gini, Mario Jerez, Oscar Nelson, Robert McPherson, Sarah Boelter, Sree Ganesh Lalitaditya Divakarla","submitted_at":"2025-05-16T00:59:31Z","abstract_excerpt":"Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments. This paper presents an integrated unmanned aerial vehicle (UAV) system designed for autonomous wildlife deterrence, developed as part of the Farm Robotics Challenge. Our system combines a YOLO-based real-time computer vision module for deer detection, an energy-efficient coverage path planning algorithm for efficient field monitoring, and an autonomous charging station for co"},"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":"2505.10770","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-05-16T00:59:31Z","cross_cats_sorted":["cs.AI","cs.MA"],"title_canon_sha256":"c8027a593412ea520c534b95da957ed9c73d6a85f2266ef38c73dee1d37dc67b","abstract_canon_sha256":"f07ff0fad488aeee431cb6c7299125440f46ff7df6eaaf4cb3a6b8fac3b8dd48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:05.287771Z","signature_b64":"im9GDrO1gWlrRrpVEh/ybyZZhyXpOXtQotzg5L8tqyPGOMAXVj2kKW9Re2ibgg3UGA7sY6LKv3BHCZ9pWZKPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da467d54efa0ce42f1d0da95b7c1288f4c7e03be6523bb3809309044112eaa33","last_reissued_at":"2026-07-05T11:04:05.287252Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:05.287252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.RO","authors_text":"Ebasa Temesgen, Graham Wilson, Greta Brown, Maria Gini, Mario Jerez, Oscar Nelson, Robert McPherson, Sarah Boelter, Sree Ganesh Lalitaditya Divakarla","submitted_at":"2025-05-16T00:59:31Z","abstract_excerpt":"Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments. This paper presents an integrated unmanned aerial vehicle (UAV) system designed for autonomous wildlife deterrence, developed as part of the Farm Robotics Challenge. Our system combines a YOLO-based real-time computer vision module for deer detection, an energy-efficient coverage path planning algorithm for efficient field monitoring, and an autonomous charging station for co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10770","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/2505.10770/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":"2505.10770","created_at":"2026-07-05T11:04:05.287311+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10770v1","created_at":"2026-07-05T11:04:05.287311+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10770","created_at":"2026-07-05T11:04:05.287311+00:00"},{"alias_kind":"pith_short_12","alias_value":"3JDH2VHPUDHE","created_at":"2026-07-05T11:04:05.287311+00:00"},{"alias_kind":"pith_short_16","alias_value":"3JDH2VHPUDHEF4OQ","created_at":"2026-07-05T11:04:05.287311+00:00"},{"alias_kind":"pith_short_8","alias_value":"3JDH2VHP","created_at":"2026-07-05T11:04:05.287311+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/3JDH2VHPUDHEF4OQ3KK3PQJIR5","json":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5.json","graph_json":"https://pith.science/api/pith-number/3JDH2VHPUDHEF4OQ3KK3PQJIR5/graph.json","events_json":"https://pith.science/api/pith-number/3JDH2VHPUDHEF4OQ3KK3PQJIR5/events.json","paper":"https://pith.science/paper/3JDH2VHP"},"agent_actions":{"view_html":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5","download_json":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5.json","view_paper":"https://pith.science/paper/3JDH2VHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10770&json=true","fetch_graph":"https://pith.science/api/pith-number/3JDH2VHPUDHEF4OQ3KK3PQJIR5/graph.json","fetch_events":"https://pith.science/api/pith-number/3JDH2VHPUDHEF4OQ3KK3PQJIR5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5/action/storage_attestation","attest_author":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5/action/author_attestation","sign_citation":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5/action/citation_signature","submit_replication":"https://pith.science/pith/3JDH2VHPUDHEF4OQ3KK3PQJIR5/action/replication_record"}},"created_at":"2026-07-05T11:04:05.287311+00:00","updated_at":"2026-07-05T11:04:05.287311+00:00"}