{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:V4U76PIFUJLKHU6YHC7KPVWZCX","short_pith_number":"pith:V4U76PIF","canonical_record":{"source":{"id":"2507.06336","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-08T18:55:11Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"2d37eb5805af464e78f8d091fe517b17d01bbc7c6e89c5f5e248a392c67208e7","abstract_canon_sha256":"d55bc364d6af720b6e2a7a822eb05b6b27368b2896c8701e1ce352168328fcf2"},"schema_version":"1.0"},"canonical_sha256":"af29ff3d05a256a3d3d838bea7d6d915ed643327ca23b839b30efaa2a3e2347c","source":{"kind":"arxiv","id":"2507.06336","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.06336","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"arxiv_version","alias_value":"2507.06336v1","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06336","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"pith_short_12","alias_value":"V4U76PIFUJLK","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"pith_short_16","alias_value":"V4U76PIFUJLKHU6Y","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"pith_short_8","alias_value":"V4U76PIF","created_at":"2026-07-05T11:34:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:V4U76PIFUJLKHU6YHC7KPVWZCX","target":"record","payload":{"canonical_record":{"source":{"id":"2507.06336","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-08T18:55:11Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"2d37eb5805af464e78f8d091fe517b17d01bbc7c6e89c5f5e248a392c67208e7","abstract_canon_sha256":"d55bc364d6af720b6e2a7a822eb05b6b27368b2896c8701e1ce352168328fcf2"},"schema_version":"1.0"},"canonical_sha256":"af29ff3d05a256a3d3d838bea7d6d915ed643327ca23b839b30efaa2a3e2347c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:13.866956Z","signature_b64":"4CDw1JYHZeTfBU1ASisCRlumE+iReJDA/6I2MliskiJ1t4ozkwYV/7OcomdkCDSd4l2+vXvX92+XsOscS3c9Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af29ff3d05a256a3d3d838bea7d6d915ed643327ca23b839b30efaa2a3e2347c","last_reissued_at":"2026-07-05T11:34:13.866417Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:13.866417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.06336","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":"2026-07-05T11:34:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c3lYRbRzeK5sivkC22YbkrIqXcEAk+60gytnIJAHrXY7mP7XYzOUXIuVyPMx9o/EcSkEX0MQe9+BXdJnrcKbDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:15:01.231172Z"},"content_sha256":"cc3f7b5c55ad9eb7673da6e78129fed6db4a7ca7e87e85a0a72852e0519604aa","schema_version":"1.0","event_id":"sha256:cc3f7b5c55ad9eb7673da6e78129fed6db4a7ca7e87e85a0a72852e0519604aa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:V4U76PIFUJLKHU6YHC7KPVWZCX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"q-bio.QM","authors_text":"Adam J Riesselman, Evan M Cofer, Therese LaRue, Wim Meeussen","submitted_at":"2025-07-08T18:55:11Z","abstract_excerpt":"Quantifying organism-level phenotypes, such as growth dynamics and biomass accumulation, is fundamental to understanding agronomic traits and optimizing crop production. However, quality growing data of plants at scale is difficult to generate. Here we use a mobile robotic platform to capture high-resolution environmental sensing and phenotyping measurements of a large-scale hydroponic leafy greens system. We describe a self-supervised modeling approach to build a map from observed growing data to the entire plant growth trajectory. We demonstrate our approach by forecasting future plant heigh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06336","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/2507.06336/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":"2026-07-05T11:34:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"czm4kmtAU4KzJH+hIodklTxXBwY112Ip4mNJWaZDJA+ZejGbduA3dmwOLuYTgQxLJ4OwJySmTPOdXu87bbfBBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:15:01.232320Z"},"content_sha256":"5d146038601c4ba42e81168c4bdc5ad646a8d4da23a74ef143161c1075935fcb","schema_version":"1.0","event_id":"sha256:5d146038601c4ba42e81168c4bdc5ad646a8d4da23a74ef143161c1075935fcb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V4U76PIFUJLKHU6YHC7KPVWZCX/bundle.json","state_url":"https://pith.science/pith/V4U76PIFUJLKHU6YHC7KPVWZCX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V4U76PIFUJLKHU6YHC7KPVWZCX/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":"2026-08-09T10:15:01Z","links":{"resolver":"https://pith.science/pith/V4U76PIFUJLKHU6YHC7KPVWZCX","bundle":"https://pith.science/pith/V4U76PIFUJLKHU6YHC7KPVWZCX/bundle.json","state":"https://pith.science/pith/V4U76PIFUJLKHU6YHC7KPVWZCX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V4U76PIFUJLKHU6YHC7KPVWZCX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V4U76PIFUJLKHU6YHC7KPVWZCX","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":"d55bc364d6af720b6e2a7a822eb05b6b27368b2896c8701e1ce352168328fcf2","cross_cats_sorted":["cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-08T18:55:11Z","title_canon_sha256":"2d37eb5805af464e78f8d091fe517b17d01bbc7c6e89c5f5e248a392c67208e7"},"schema_version":"1.0","source":{"id":"2507.06336","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.06336","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"arxiv_version","alias_value":"2507.06336v1","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06336","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"pith_short_12","alias_value":"V4U76PIFUJLK","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"pith_short_16","alias_value":"V4U76PIFUJLKHU6Y","created_at":"2026-07-05T11:34:13Z"},{"alias_kind":"pith_short_8","alias_value":"V4U76PIF","created_at":"2026-07-05T11:34:13Z"}],"graph_snapshots":[{"event_id":"sha256:5d146038601c4ba42e81168c4bdc5ad646a8d4da23a74ef143161c1075935fcb","target":"graph","created_at":"2026-07-05T11:34:13Z","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/2507.06336/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Quantifying organism-level phenotypes, such as growth dynamics and biomass accumulation, is fundamental to understanding agronomic traits and optimizing crop production. However, quality growing data of plants at scale is difficult to generate. Here we use a mobile robotic platform to capture high-resolution environmental sensing and phenotyping measurements of a large-scale hydroponic leafy greens system. We describe a self-supervised modeling approach to build a map from observed growing data to the entire plant growth trajectory. We demonstrate our approach by forecasting future plant heigh","authors_text":"Adam J Riesselman, Evan M Cofer, Therese LaRue, Wim Meeussen","cross_cats":["cs.LG","cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-08T18:55:11Z","title":"Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06336","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:cc3f7b5c55ad9eb7673da6e78129fed6db4a7ca7e87e85a0a72852e0519604aa","target":"record","created_at":"2026-07-05T11:34:13Z","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":"d55bc364d6af720b6e2a7a822eb05b6b27368b2896c8701e1ce352168328fcf2","cross_cats_sorted":["cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-07-08T18:55:11Z","title_canon_sha256":"2d37eb5805af464e78f8d091fe517b17d01bbc7c6e89c5f5e248a392c67208e7"},"schema_version":"1.0","source":{"id":"2507.06336","kind":"arxiv","version":1}},"canonical_sha256":"af29ff3d05a256a3d3d838bea7d6d915ed643327ca23b839b30efaa2a3e2347c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af29ff3d05a256a3d3d838bea7d6d915ed643327ca23b839b30efaa2a3e2347c","first_computed_at":"2026-07-05T11:34:13.866417Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:34:13.866417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4CDw1JYHZeTfBU1ASisCRlumE+iReJDA/6I2MliskiJ1t4ozkwYV/7OcomdkCDSd4l2+vXvX92+XsOscS3c9Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:34:13.866956Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.06336","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc3f7b5c55ad9eb7673da6e78129fed6db4a7ca7e87e85a0a72852e0519604aa","sha256:5d146038601c4ba42e81168c4bdc5ad646a8d4da23a74ef143161c1075935fcb"],"state_sha256":"51a3a1612dd0aa37740926e0eb26d2d4fd5a268d17b7ddd897563ef56d795a56"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J8v72Z4TefSfjGqbhIan7APtO3gacV7k0Q4h2VpKem/qemvY8wM8vi3zQJJ/2KfbgJSmsV3IqBiNp4hk3Rs6CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T10:15:01.240154Z","bundle_sha256":"2098ec2b804e032c3af1eea6192b8b49f4f20eb611d292a313359159c77d53ab"}}