{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:53B5T7XFBCDGKYZKOQEREQIJAZ","short_pith_number":"pith:53B5T7XF","canonical_record":{"source":{"id":"2105.10325","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-21T12:57:48Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"8b3726f14ba1afb323b789a8417d0fbf279101fcce6f5f33873d139065765c2d","abstract_canon_sha256":"a35dd996ec2db06cfc660890dec8d1a5545c212e9be3c2cbbc92c489c246211b"},"schema_version":"1.0"},"canonical_sha256":"eec3d9fee5088665632a74091241090647ddf6d4b59abafb9554d0731eb4f357","source":{"kind":"arxiv","id":"2105.10325","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.10325","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"arxiv_version","alias_value":"2105.10325v1","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10325","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"pith_short_12","alias_value":"53B5T7XFBCDG","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"pith_short_16","alias_value":"53B5T7XFBCDGKYZK","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"pith_short_8","alias_value":"53B5T7XF","created_at":"2026-07-05T04:08:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:53B5T7XFBCDGKYZKOQEREQIJAZ","target":"record","payload":{"canonical_record":{"source":{"id":"2105.10325","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-21T12:57:48Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"8b3726f14ba1afb323b789a8417d0fbf279101fcce6f5f33873d139065765c2d","abstract_canon_sha256":"a35dd996ec2db06cfc660890dec8d1a5545c212e9be3c2cbbc92c489c246211b"},"schema_version":"1.0"},"canonical_sha256":"eec3d9fee5088665632a74091241090647ddf6d4b59abafb9554d0731eb4f357","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:08:24.255721Z","signature_b64":"DFGOIDFL/t20IjQCyUvdi5tEsPxx7P7yZoEDBcY/P8twtHvEUHSbML/wer+za4cEed0Ia7GUiu15SyONOozjAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eec3d9fee5088665632a74091241090647ddf6d4b59abafb9554d0731eb4f357","last_reissued_at":"2026-07-05T04:08:24.255265Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:08:24.255265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.10325","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":"2026-07-05T04:08:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"77kBzhfCZNMssA4XwLyvE1ustWultSCcn3THW6YcHUg1QlpTlsRJ1Uo2tpzqKaJOaWln9z33BUfdSOFvpqM4BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:39:10.329708Z"},"content_sha256":"3eeebdca7f7fed7ba3bd68566a95efe194fe807c919858a5414ef44aaad9fa47","schema_version":"1.0","event_id":"sha256:3eeebdca7f7fed7ba3bd68566a95efe194fe807c919858a5414ef44aaad9fa47"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:53B5T7XFBCDGKYZKOQEREQIJAZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Anna Kicherer, Immanuel Weber, Jana Kierdorf, Laura Zabawa, Lukas Drees, Ribana Roscher","submitted_at":"2021-05-21T12:57:48Z","abstract_excerpt":"The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a likely s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10325","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/2105.10325/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":"2026-07-05T04:08:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aMEehZmQmW15w1EOMwsHd9LD31VvyFf6l54ppENrM3FRrhbpPw6xGc+D/DMFi3WN/307ddki1TJSZMz9pTR3BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:39:10.331009Z"},"content_sha256":"f61dfa60f1761c9af1527fada8e02e1ace22e17b4a7dacf965abd017b3cfda71","schema_version":"1.0","event_id":"sha256:f61dfa60f1761c9af1527fada8e02e1ace22e17b4a7dacf965abd017b3cfda71"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/53B5T7XFBCDGKYZKOQEREQIJAZ/bundle.json","state_url":"https://pith.science/pith/53B5T7XFBCDGKYZKOQEREQIJAZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/53B5T7XFBCDGKYZKOQEREQIJAZ/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":"2026-08-15T18:39:10Z","links":{"resolver":"https://pith.science/pith/53B5T7XFBCDGKYZKOQEREQIJAZ","bundle":"https://pith.science/pith/53B5T7XFBCDGKYZKOQEREQIJAZ/bundle.json","state":"https://pith.science/pith/53B5T7XFBCDGKYZKOQEREQIJAZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/53B5T7XFBCDGKYZKOQEREQIJAZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:53B5T7XFBCDGKYZKOQEREQIJAZ","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":"a35dd996ec2db06cfc660890dec8d1a5545c212e9be3c2cbbc92c489c246211b","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-21T12:57:48Z","title_canon_sha256":"8b3726f14ba1afb323b789a8417d0fbf279101fcce6f5f33873d139065765c2d"},"schema_version":"1.0","source":{"id":"2105.10325","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.10325","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"arxiv_version","alias_value":"2105.10325v1","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10325","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"pith_short_12","alias_value":"53B5T7XFBCDG","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"pith_short_16","alias_value":"53B5T7XFBCDGKYZK","created_at":"2026-07-05T04:08:24Z"},{"alias_kind":"pith_short_8","alias_value":"53B5T7XF","created_at":"2026-07-05T04:08:24Z"}],"graph_snapshots":[{"event_id":"sha256:f61dfa60f1761c9af1527fada8e02e1ace22e17b4a7dacf965abd017b3cfda71","target":"graph","created_at":"2026-07-05T04:08:24Z","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/2105.10325/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a likely s","authors_text":"Anna Kicherer, Immanuel Weber, Jana Kierdorf, Laura Zabawa, Lukas Drees, Ribana Roscher","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-21T12:57:48Z","title":"Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10325","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":"sha256:3eeebdca7f7fed7ba3bd68566a95efe194fe807c919858a5414ef44aaad9fa47","target":"record","created_at":"2026-07-05T04:08:24Z","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":"a35dd996ec2db06cfc660890dec8d1a5545c212e9be3c2cbbc92c489c246211b","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-21T12:57:48Z","title_canon_sha256":"8b3726f14ba1afb323b789a8417d0fbf279101fcce6f5f33873d139065765c2d"},"schema_version":"1.0","source":{"id":"2105.10325","kind":"arxiv","version":1}},"canonical_sha256":"eec3d9fee5088665632a74091241090647ddf6d4b59abafb9554d0731eb4f357","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eec3d9fee5088665632a74091241090647ddf6d4b59abafb9554d0731eb4f357","first_computed_at":"2026-07-05T04:08:24.255265Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:08:24.255265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DFGOIDFL/t20IjQCyUvdi5tEsPxx7P7yZoEDBcY/P8twtHvEUHSbML/wer+za4cEed0Ia7GUiu15SyONOozjAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:08:24.255721Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.10325","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3eeebdca7f7fed7ba3bd68566a95efe194fe807c919858a5414ef44aaad9fa47","sha256:f61dfa60f1761c9af1527fada8e02e1ace22e17b4a7dacf965abd017b3cfda71"],"state_sha256":"2ced0855fe3ca8714cd1ddc18c153c8dd5ee9f7cd6fe5894e55d40a4bfad5e0b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vEl5iqh62MJQbdRju30mRunpwLsv0Xj6ODn3MumX4h4mO2eGl2Hjq9yFjeKXWbXChDlGSXWTsfqfZP3bwJEuCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T18:39:10.336302Z","bundle_sha256":"acd22dd4d799ddc1573b74c69db9b3663ddd17fc8842133eb401e9f279250279"}}