{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4DHSOFYEHH4GJLYKJHRUSOQRW4","short_pith_number":"pith:4DHSOFYE","canonical_record":{"source":{"id":"2506.00154","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-30T18:45:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c6faf88c815fd7ac071c2c2fc004e5857830e22e463ee0b8a7304b86f37b7af1","abstract_canon_sha256":"a09f20b9f1c6c19a9761a07dbedd50ca229369d101fb2d6bef9a04e65adc8740"},"schema_version":"1.0"},"canonical_sha256":"e0cf27170439f864af0a49e3493a11b7234c489a40db3b6acca700f682314af3","source":{"kind":"arxiv","id":"2506.00154","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00154","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00154v1","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00154","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"pith_short_12","alias_value":"4DHSOFYEHH4G","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"pith_short_16","alias_value":"4DHSOFYEHH4GJLYK","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"pith_short_8","alias_value":"4DHSOFYE","created_at":"2026-07-05T11:13:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4DHSOFYEHH4GJLYKJHRUSOQRW4","target":"record","payload":{"canonical_record":{"source":{"id":"2506.00154","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-30T18:45:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c6faf88c815fd7ac071c2c2fc004e5857830e22e463ee0b8a7304b86f37b7af1","abstract_canon_sha256":"a09f20b9f1c6c19a9761a07dbedd50ca229369d101fb2d6bef9a04e65adc8740"},"schema_version":"1.0"},"canonical_sha256":"e0cf27170439f864af0a49e3493a11b7234c489a40db3b6acca700f682314af3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:29.910796Z","signature_b64":"46HGzTv7ZSNEiWjNyW44JqFV4sdLT7H0/RVYs8XCm41lQoo5onVw9TuNPjswqra+XeUpNjs6j2MfUe0BCfyUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0cf27170439f864af0a49e3493a11b7234c489a40db3b6acca700f682314af3","last_reissued_at":"2026-07-05T11:13:29.910300Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:29.910300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.00154","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:13:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lhjNRu0FWZSXTNkkFh+53q+0CvbwM35g7d0ehQM7HXVmxifr7tNiMsNYQsAkKAfMLfCyi1abUbmWeQLvk7jhDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:58:52.375162Z"},"content_sha256":"49f0592d8da043a8e65dee6c44f11b8fd4b7f762c84cae79f839043368e6dbe0","schema_version":"1.0","event_id":"sha256:49f0592d8da043a8e65dee6c44f11b8fd4b7f762c84cae79f839043368e6dbe0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4DHSOFYEHH4GJLYKJHRUSOQRW4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Agust\\'in Roca, Gabriel Torre, Gast\\'on Castro, Ignacio Mas, Javier Pereira, Juan I. Giribet, Leonardo J. Colombo","submitted_at":"2025-05-30T18:45:42Z","abstract_excerpt":"This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and are occluded by vegetation. We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included. Experimental results show the effectiveness of incorporating the segmentation head achieving superior detection performance. This work contributes valuable insights for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00154","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/2506.00154/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:13:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bmA4iWNbABj8FJo5tSi8amsf+OmON8ToPcspPOj1dzWPTlWhXK4Dfl908Wp3W7s2wHxOn3TTCz55OXbR/y4YDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:58:52.375672Z"},"content_sha256":"ee619f252bb7bed5fdb1f441f9a053391ee6e2cfd9d5ae950d4c0ecc9fb33a78","schema_version":"1.0","event_id":"sha256:ee619f252bb7bed5fdb1f441f9a053391ee6e2cfd9d5ae950d4c0ecc9fb33a78"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4DHSOFYEHH4GJLYKJHRUSOQRW4/bundle.json","state_url":"https://pith.science/pith/4DHSOFYEHH4GJLYKJHRUSOQRW4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4DHSOFYEHH4GJLYKJHRUSOQRW4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T20:58:52Z","links":{"resolver":"https://pith.science/pith/4DHSOFYEHH4GJLYKJHRUSOQRW4","bundle":"https://pith.science/pith/4DHSOFYEHH4GJLYKJHRUSOQRW4/bundle.json","state":"https://pith.science/pith/4DHSOFYEHH4GJLYKJHRUSOQRW4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4DHSOFYEHH4GJLYKJHRUSOQRW4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4DHSOFYEHH4GJLYKJHRUSOQRW4","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a09f20b9f1c6c19a9761a07dbedd50ca229369d101fb2d6bef9a04e65adc8740","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-30T18:45:42Z","title_canon_sha256":"c6faf88c815fd7ac071c2c2fc004e5857830e22e463ee0b8a7304b86f37b7af1"},"schema_version":"1.0","source":{"id":"2506.00154","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00154","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00154v1","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00154","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"pith_short_12","alias_value":"4DHSOFYEHH4G","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"pith_short_16","alias_value":"4DHSOFYEHH4GJLYK","created_at":"2026-07-05T11:13:29Z"},{"alias_kind":"pith_short_8","alias_value":"4DHSOFYE","created_at":"2026-07-05T11:13:29Z"}],"graph_snapshots":[{"event_id":"sha256:ee619f252bb7bed5fdb1f441f9a053391ee6e2cfd9d5ae950d4c0ecc9fb33a78","target":"graph","created_at":"2026-07-05T11:13:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.00154/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and are occluded by vegetation. We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included. Experimental results show the effectiveness of incorporating the segmentation head achieving superior detection performance. This work contributes valuable insights for ","authors_text":"Agust\\'in Roca, Gabriel Torre, Gast\\'on Castro, Ignacio Mas, Javier Pereira, Juan I. Giribet, Leonardo J. Colombo","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-30T18:45:42Z","title":"Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00154","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:49f0592d8da043a8e65dee6c44f11b8fd4b7f762c84cae79f839043368e6dbe0","target":"record","created_at":"2026-07-05T11:13:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a09f20b9f1c6c19a9761a07dbedd50ca229369d101fb2d6bef9a04e65adc8740","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-30T18:45:42Z","title_canon_sha256":"c6faf88c815fd7ac071c2c2fc004e5857830e22e463ee0b8a7304b86f37b7af1"},"schema_version":"1.0","source":{"id":"2506.00154","kind":"arxiv","version":1}},"canonical_sha256":"e0cf27170439f864af0a49e3493a11b7234c489a40db3b6acca700f682314af3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e0cf27170439f864af0a49e3493a11b7234c489a40db3b6acca700f682314af3","first_computed_at":"2026-07-05T11:13:29.910300Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:13:29.910300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"46HGzTv7ZSNEiWjNyW44JqFV4sdLT7H0/RVYs8XCm41lQoo5onVw9TuNPjswqra+XeUpNjs6j2MfUe0BCfyUCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:13:29.910796Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.00154","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49f0592d8da043a8e65dee6c44f11b8fd4b7f762c84cae79f839043368e6dbe0","sha256:ee619f252bb7bed5fdb1f441f9a053391ee6e2cfd9d5ae950d4c0ecc9fb33a78"],"state_sha256":"4f7221795e39b059f0b1f3eb3c7dabfc5b6ff9432cfd66c3dae6619974c0f312"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n1+KSvewYtDRbeiN4HsIiChUJGRGd4oOr8gtul8crgRE4i7ZmwGPi+ftLNJmbTvg3EjeeR+/qdemd6pWHP/wDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:58:52.379602Z","bundle_sha256":"bd9ebfbbbed9f554ad77e1d5b9220fb04b95939a91118d6546d234311b8f4dd4"}}