{"as_of":"2026-08-10T00:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1b5c6827ce37471697defcaaef66ceb1050a3d06babc0d5e6faa243e8415976d","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:27:41.283002Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T04:37:52.241536Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.10919","snapshot_observed_at":"2026-08-02T04:37:52.241536Z","title":"Lightweight metadata-aware Mixture-of-Experts masked autoencoder for earth observation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.13651","last_updated":"2026-07-15T09:52:22Z","snapshot_observed_at":"2026-08-09T22:13:38.681501Z","submitted_at":"2026-07-15T09:52:22Z","title":"From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T04:37:52.241536Z"},"links":{"cited_paper":"/paper/2509.10919","citing_paper":"/paper/2607.13651"},"observation_digest":"sha256:cd02f358a9d6fa2a0f5a42ddbe949adab2c7d191867369d45b097d3d7f4efd74","observation_id":"42c2dd0c-c448-4112-95e1-7bce21180a80","resolution":{"observed_at":"2026-08-02T04:37:52.241536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2509.10919/citation-record","integrity":"/paper/2509.10919/integrity","json":"/paper/2509.10919/citation-record.json","paper":"/paper/2509.10919"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:39.315118Z","title":"Lobell, and Stefano Ermon","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:39.315118Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:8817dfd90a2cb08fb0bb6e2315b5df1ebdceb84eeba97add2fe7a99d2393d8e9","observation_id":"9f17e959-dd86-4e22-bcce-7d60187b34c9","resolution":{"observed_at":"2026-08-04T17:27:39.315118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:39.512188Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:39.512188Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:e242bc3ecc75bb7c6b69eb95465dee8edb24d203e51d21e5508797015ba17585","observation_id":"6e197fd9-db33-48fe-9091-c301d5ae4cd6","resolution":{"observed_at":"2026-08-04T17:27:39.512188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:39.635717Z","title":"TerraMind: Large-scale generative multimodality for Earth Observation, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:39.635717Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:12630f43895e6d5c9b5aa66a8a6243edda5cbe5dba943495993ff1b167ead973","observation_id":"287e74b5-9abd-4494-ac52-03b82e93510c","resolution":{"observed_at":"2026-08-04T17:27:39.635717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:39.819661Z","title":"Clay foundation model","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:39.819661Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:7ddd4b85f877dba061c87d2f6ab080734147fea1883c099b7b812fb3e4f03ffc","observation_id":"c40a5c87-6869-4ae4-8c8e-61eaa4cdc9d6","resolution":{"observed_at":"2026-08-04T17:27:39.819661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.011528Z","title":"Albrecht, and Xiao Xiang Zhu","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.011528Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:7e56231c48f735f0ab67bf70c07dd77ddb9207a5d571c2ea999c9614ddd2128d","observation_id":"253d8b6b-bffc-487c-b5f6-2faddeea7adb","resolution":{"observed_at":"2026-08-04T17:27:40.011528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.166281Z","title":"Stewart, Nils Lehmann, Isaac A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.166281Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:d285f79bf2c051b3c1cc296918ce8e104e7131e6e64a74da12b9284ae66737cf","observation_id":"df3dc81f-94ed-4931-8335-8b40e226edf3","resolution":{"observed_at":"2026-08-04T17:27:40.166281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.247478Z","title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.247478Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:3e88b83bef70b41b5a87984c0c96b4587511d8f7365268a2f6c4e3d9ba9cdbd6","observation_id":"3c449f49-b240-4436-ad90-3616f8e1dd8d","resolution":{"observed_at":"2026-08-04T17:27:40.247478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.332893Z","title":"Scaling vision with sparse Mixture of Experts, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.332893Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:3a41ff3122cae2b2fb4ff6e0224ad54048f4391bc2aed2479abfb4569b15aad0","observation_id":"dfc40728-a05c-4b04-8f42-c2c82a2609a9","resolution":{"observed_at":"2026-08-04T17:27:40.332893Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.441044Z","title":"How lightweight can a Vision Transformer be, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.441044Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:a3568653363a545608bc41f5115dfbd60c9c3a21db7b9fe9108d310a4706c7a1","observation_id":"e88fd165-6acd-4a3d-9288-1c963a7d5e34","resolution":{"observed_at":"2026-08-04T17:27:40.441044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.540382Z","title":"Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.540382Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:23cdcdba0f2bc9891fbb53946569078c51bdc89338d6329b99ff5d6e4907d46b","observation_id":"430d46d0-25b5-44a1-b099-fde88bb0cae7","resolution":{"observed_at":"2026-08-04T17:27:40.540382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.580027Z","title":"Senpa-MAE: Sensor parameter aware masked autoencoder for multi-satellite self-supervised pretraining, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.580027Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:d0446ae9964d5310a1110fdc348e1f03b8c3116e37380199e1d28c72e063021e","observation_id":"8b48ac75-447a-478a-be69-9aaa9f8d5450","resolution":{"observed_at":"2026-08-04T17:27:40.580027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.648442Z","title":"Masked autoencoders are scalable vision learners, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.648442Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:65ba76da736047a50dd3577edc1e8f901439c6858783d2dc45bef8123095b3f1","observation_id":"f524aaf1-a12e-4886-b34e-34a4e59642ea","resolution":{"observed_at":"2026-08-04T17:27:40.648442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.720143Z","title":"GLU variants improve transformer, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.720143Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:3b1d973a1e7259029e6d45a406346e8e898e64ba4d85da530c40a1705e9dee40","observation_id":"c02d139c-e9c9-4f4d-b092-b39d38c2ccaa","resolution":{"observed_at":"2026-08-04T17:27:40.720143Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.799532Z","title":"Outrageously large neural networks: The sparsely-gated Mixture-of-Experts layer, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.799532Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:beb96c3fbdfcac6e5e6a2b55e16735438472ec20370018066ab29d948858435b","observation_id":"8ba09598-51f7-4953-8b4c-f469c1af94b8","resolution":{"observed_at":"2026-08-04T17:27:40.799532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.868553Z","title":"Landsat-Bench: Datasets and benchmarks for Landsat foundation models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.868553Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:0c7704af16cf76091c9e363681b81063d40135572c57f000fe271b497828fb9d","observation_id":"3f759028-0395-4bf7-a592-df0ab1777f39","resolution":{"observed_at":"2026-08-04T17:27:40.868553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:40.926745Z","title":"BigEarthNet: A large-scale benchmark archive for remote sensing image understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:40.926745Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:e06a69bbdd8cb53dc69cdd6c53a275b68cafb0b7e1c8a14c557a9def5c682cfd","observation_id":"f61ff8af-49ff-45af-90fa-a4923799115f","resolution":{"observed_at":"2026-08-04T17:27:40.926745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:41.010839Z","title":"EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:41.010839Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:693d24b0b945430e2d7cb440ed3d4d569c2e4e007534acd2f852da68d99a3186","observation_id":"372dff28-05b4-4c53-a000-1ce29fd36ffa","resolution":{"observed_at":"2026-08-04T17:27:41.010839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:41.087589Z","title":"Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, and Xiao Xiang Zhu","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:41.087589Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:fbd9f69f58a6246e4f1e628c69df6fbb83298b24bba0c07325a76181d52b112a","observation_id":"07ea1e59-8ffc-4726-b9c9-479b9ed0c37f","resolution":{"observed_at":"2026-08-04T17:27:41.087589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:41.222216Z","title":"SatlasPretrain: A large-scale dataset for remote sensing image understanding, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:41.222216Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:7ec8cedd6d9ac7f5c9271f5efaf7549bfad65a1da072a036a3d943772d539d1e","observation_id":"ccb603da-b0bf-49e0-b9d7-1c793a384a50","resolution":{"observed_at":"2026-08-04T17:27:41.222216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:27:41.283002Z","title":"Prithvi-EO-2.0: A versatile multi-temporal foundation model for Earth Observation applications","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T17:27:41.283002Z"},"links":{"citing_paper":"/paper/2509.10919"},"observation_digest":"sha256:6a392639e1ad5744fd14bdbe65a2a47bce356d4dbb4ead9a277fd1878535d6ff","observation_id":"f48bd4c2-b2d0-4ff3-a00e-2bde81e6d852","resolution":{"observed_at":"2026-08-04T17:27:41.283002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.10919","last_updated":"2025-09-13T17:35:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T17:27:38.821947Z","submitted_at":"2025-09-13T17:35:17Z","title":"Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2509.10919."}