{"as_of":"2026-08-15T12:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fa55b394f0c6a5d95d8827a8139fe0f31be94c0a66d700b23a237bbf65a014ca","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T20:27:15.912616Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2411.09779/citation-record","integrity":"/paper/2411.09779/integrity","json":"/paper/2411.09779/citation-record.json","paper":"/paper/2411.09779"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.463703Z","title":"The major transitions in evolution revisited","venue":null,"work_id":"dd7696b6-8775-49a5-8b5b-7facfd364541","year":2011},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.770548Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:50e45b813bfdbfd63ca11d0e49207308279bb18947aeeae75a8949b1a8c9184c","observation_id":"0ecb6a64-732a-4ce2-94ab-48d476884afa","resolution":{"observed_at":"2026-08-12T20:27:16.468911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.446078Z","title":"Evolution and the levels of selection","venue":null,"work_id":"36753ee8-a667-4f78-8131-feddd56419a1","year":2006},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.776788Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:86bf0895f7fb4e8cb56e33616df63d712d8806921dcda473dfde63bd0fcae015","observation_id":"caa4a177-ffed-40bc-bc83-b96f263500d1","resolution":{"observed_at":"2026-08-12T20:27:16.451451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.430248Z","title":"Cancer and the levels of selection","venue":null,"work_id":"00f92653-fe72-4498-9177-ea35344cfbd2","year":2021},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.782436Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:9e3c7fb582c3b62a846a53c25f65d705a188e493056c9722e3a9336ef6c2db9d","observation_id":"094e3a02-f3f4-4d2a-b304-f38a408d07fc","resolution":{"observed_at":"2026-08-12T20:27:16.435468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.413396Z","title":"Compositional evolution: the impact of sex, symbiosis and modularity on the gradualist framework of evolution","venue":null,"work_id":"f895c0ff-6c60-4b15-b6bc-547e556cc26d","year":2006},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.788224Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:b85653b4a8c86bbcec0845b48afdd4431dbc27f9345ff9c6dfe9d17478de5dae","observation_id":"bf69f481-dc34-484e-819b-e95f62b3d230","resolution":{"observed_at":"2026-08-12T20:27:16.418932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.397590Z","title":"Hierarchically consistent test problems for genetic algorithms","venue":null,"work_id":"fab24d29-4592-428e-bdc7-658ded36543e","year":1999},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.794067Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:0e2dd4e0bc9cac371ab0a10ca215b7f8ea8b0c7f0fb931768732024cc94a8306","observation_id":"561479ad-a990-4c34-b7f3-7e873a26b39a","resolution":{"observed_at":"2026-08-12T20:27:16.402781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.380985Z","title":"Evolution of cooperation by multilevel selection","venue":null,"work_id":"f31889f4-7b35-40c7-bbd6-a077e9b30be2","year":2006},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.799848Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:d8fd44a0f84b36df0f6791f5484fc490918aa2db599b1f5a226b7187c6ea33da","observation_id":"320e3a46-7146-44ff-9f97-f16c747e52b1","resolution":{"observed_at":"2026-08-12T20:27:16.385970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.362758Z","title":"The generalized Price equation: forces that change population statistics","venue":null,"work_id":"51335bf7-6167-45b6-8578-9355697634bc","year":2020},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.805627Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:391fa723ea862ee5d18fcecb7e0a95a031e61706a59ba6fa89d686e1e7c95027","observation_id":"41cc9fe6-4df6-4b3d-b11b-c0857b5068e3","resolution":{"observed_at":"2026-08-12T20:27:16.368630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.345738Z","title":"Cyclic and multilevel causation in evolutionary pro- cesses","venue":null,"work_id":"8a546de2-dce3-4082-8409-6223e70d88be","year":2020},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.811149Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:78b18bb873f963bb14d4e36f45f35cf02a4031030845483f4728ffe712d622d4","observation_id":"2343e9e8-b8d0-4665-8157-14135c28206e","resolution":{"observed_at":"2026-08-12T20:27:16.351499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.328116Z","title":"The metric monad for probabilistic nondeterminism","venue":null,"work_id":"dda7db1c-ae51-49fe-9b92-dc9be1d0166f","year":2005},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.817140Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:9b2bac1b58e9b4c42043f0084fb4993083aaa2170159f2e89577eb6f8e0399e1","observation_id":"309f579e-e945-446d-8162-fc77f2917673","resolution":{"observed_at":"2026-08-12T20:27:16.333854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-08-14T20:16:55.687026Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-12T20:27:15.822320Z","title":"Wasserstein auto-encoders","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.822320Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:a78e4b5999bd6497545706ac1c094e4ad64bce1e6dd1fd365530587899bf885b","observation_id":"474bf042-7f52-4875-a821-ee64deb00ecd","resolution":{"observed_at":"2026-08-12T20:27:15.822320Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.307413Z","title":"Inferring phylogenies","venue":null,"work_id":"f45d245a-35e5-491a-97d0-37eed3762c3d","year":2004},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.827916Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:626b51c50d666be47a62abd942bd5d681a7eaf35a8bfe38314e612dfc1f001f0","observation_id":"d4b43ce0-d3af-4127-b8db-37b9bbaa3f5f","resolution":{"observed_at":"2026-08-12T20:27:16.314052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.287021Z","title":"Variational combinatorial sequential Monte Carlo methods for Bayesian phylogenetic inference","venue":null,"work_id":"17ed8659-6319-4b6d-9eac-c57b299496bb","year":2021},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.832872Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:21ed9a968cac3d32ff0259feb680491bd7ce3b04b69529345cbd2ad646ab0377","observation_id":"08956d42-700f-4048-bd06-c970010e80d9","resolution":{"observed_at":"2026-08-12T20:27:16.293986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.266401Z","title":"Variational Bayesian phylogenetic inference","venue":null,"work_id":"f987e893-b519-49ea-a238-593b1627c977","year":2018},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.838461Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:84e4bd981c57f422849ecbc619336052436e51dd66958c1323b4560e10dfc508","observation_id":"f48fc126-f29a-46a8-b56f-41426ef2b729","resolution":{"observed_at":"2026-08-12T20:27:16.273713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.248166Z","title":"Improved variational Bayesian phylogenetic inference with normalizing flows","venue":null,"work_id":"5fee755b-7fff-4e68-8c37-19a4ba848d04","year":2020},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.843356Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:f2ae9bf45ca842af24ad29f13e5f94d3942c6475b19ba5c6d980212790478ea8","observation_id":"bb7c734f-5985-46ef-8502-90f7fca17c78","resolution":{"observed_at":"2026-08-12T20:27:16.253718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.230080Z","title":"Smoothing-based optimization","venue":null,"work_id":"345d5381-d40c-4016-a76d-7dfb158a18dc","year":2008},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.848097Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:7d7c02ea28eb0bc297f04b11c81b38a835c0a6e58979110e8f5695cfff9cde65","observation_id":"deac4a98-db94-48c4-9f25-a6f07dd5e5db","resolution":{"observed_at":"2026-08-12T20:27:16.236001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.205964Z","title":"Evolutionary Theory: Mathematical and conceptual foundations","venue":null,"work_id":"5d10e4b5-df93-49e8-900b-03dc32d3056f","year":2004},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.853835Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:ed4b732a9b8b7dbbcf4884bcfb093b3e4764815dabe89f93d98e2a1d0ce465e8","observation_id":"96a1b231-ee5a-4156-89f6-54b3de712a9b","resolution":{"observed_at":"2026-08-12T20:27:16.213268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.186845Z","title":"How Many Levels Are There? How Insights from Evolutionary Transitions in Individuality Help Measure the Hierarchical Complexity of Life","venue":null,"work_id":"8a288a15-3d2d-4544-bc09-cb38c71f943b","year":2011},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.860217Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:c6ce57ca10cfb433dd57875c17282ed89e2e63f22d361a82c5e1cab7a969138e","observation_id":"ea0ddf7a-7227-4ae6-bfc2-21cd8b1eeb9c","resolution":{"observed_at":"2026-08-12T20:27:16.192063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.170342Z","title":"Stochastic optimization, stochastic approximation and simulated annealing","venue":null,"work_id":"53d60d9a-5809-4a2a-8512-ac314db5ae75","year":2001},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.865698Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:fb7fd1a80b62c74e948a199b690bdd778b1f6aef102f2ce145d27f68d74536f8","observation_id":"18579b72-08c1-4312-b53b-fbc636b1e1d2","resolution":{"observed_at":"2026-08-12T20:27:16.175709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00945","last_updated":"2024-01-01T19:41:30Z","snapshot_observed_at":"2026-08-15T09:01:32.180318Z","submitted_at":"2024-01-01T19:41:30Z","title":"A review of Monte Carlo-based versions of the EM algorithm","version":1},"cited_work":{"arxiv_id":"2401.00945","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.00945","snapshot_observed_at":"2026-08-12T20:27:15.973720Z","title":"A review of Monte Carlo-based versions of the EM algorithm","venue":"stat.CO","work_id":"e02a953c-b1be-47ad-974d-3b14a2c29558","year":2024},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.870841Z"},"links":{"cited_paper":"/paper/2401.00945","citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:f4b9496ec9296e62795aeadd403bde67a40e14bd96514ab313772fe81acfb57f","observation_id":"426e1f65-a757-4e81-8c0d-da6b894c35fd","resolution":{"observed_at":"2026-08-12T20:27:15.982048Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.153277Z","title":"Passenger mutations in more than 2,500 cancer genomes: overall molecular functional impact and consequences","venue":null,"work_id":"b72e7018-d105-454a-8855-775329cc947b","year":2020},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.876340Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:f1fc5f599306a94e697d91cfa1f3dd109fbfbbb94370cfd83a5ecbf2d297d9e1","observation_id":"ec3ce7fd-2059-4196-8b9d-16a17d9abfc4","resolution":{"observed_at":"2026-08-12T20:27:16.158537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.134508Z","title":"Estimating growth patterns and driver effects in tumor evolution from individual samples","venue":null,"work_id":"1c967d15-d2fc-4962-b53c-122f053650cc","year":2020},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.882531Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:491ef7ff7fb3c199951777f0f4d7747cc1676632d782b83dc0b25c92f00b74e7","observation_id":"8fff98d2-0760-435e-99dc-7a93037f40e0","resolution":{"observed_at":"2026-08-12T20:27:16.140690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.115537Z","title":"Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution","venue":null,"work_id":"76877cd2-14b7-4e7a-8b00-cf81e8d5fbf9","year":2022},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.887801Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:53c2322413fbff239d92d8d840982eec96176279bd46e1ca3d05f46bb3d0a02b","observation_id":"a94945e1-a8d4-42b2-86e7-558195ed2271","resolution":{"observed_at":"2026-08-12T20:27:16.121094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.097874Z","title":"CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data","venue":null,"work_id":"89588471-6a69-4cb1-862c-92775fbb4f4d","year":2021},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.892666Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:e06aaee3f0d83e0ad46cd2173d6eb3c9ee8412fd04330186d23b1c258f2e4bc4","observation_id":"d10a25f7-aafc-4751-991d-c1040ebd51ff","resolution":{"observed_at":"2026-08-12T20:27:16.103676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.079867Z","title":"Microbial life history: the fundamental forces of biological design","venue":null,"work_id":"1d863db7-948e-4124-9503-80ac8cc1fbd5","year":2022},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.897496Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:56a9b03fe11227395c57927e5f4ff44573ed6b0445fc18c09f447150a3e76f7e","observation_id":"5a730808-3439-4048-b493-14d010d41712","resolution":{"observed_at":"2026-08-12T20:27:16.085324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.037766Z","title":"The information theory of individuality","venue":null,"work_id":"8bb1b04c-e079-4ad8-ac77-fe2d3406d002","year":2020},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.902628Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:65fcb61e9fd406192b232d65503922993690be1baac4ec70389a41de78595368","observation_id":"c6a6a97c-dcfe-4ac9-9f87-a13381f3e7ae","resolution":{"observed_at":"2026-08-12T20:27:16.043827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:27:16.017608Z","title":"Implicit bilevel optimization: differentiating through bilevel optimization programming","venue":null,"work_id":"1e40b151-a189-42a1-bcd8-e9a15bf72460","year":2023},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.907646Z"},"links":{"citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:8ee9c84943381cd3bba1e376d7f9bc2000b87f1fd7444bbc5ae8368a014a42fa","observation_id":"a13eb2a8-95d5-4285-a4d7-6fa313d2205b","resolution":{"observed_at":"2026-08-12T20:27:16.024844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15332","last_updated":"2024-06-06T00:58:55Z","snapshot_observed_at":"2026-08-14T14:50:36.475886Z","submitted_at":"2024-02-23T14:01:53Z","title":"Position: Categorical Deep Learning is an Algebraic Theory of All Architectures","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15332","snapshot_observed_at":"2026-08-12T20:27:15.912616Z","title":"Categorical deep learning: An algebraic theory of architectures","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T20:27:15.912616Z"},"links":{"cited_paper":"/paper/2402.15332","citing_paper":"/paper/2411.09779"},"observation_digest":"sha256:79fd391d7e607d26d382175c0e39dd1f315b4e35de40bea0a183e9f2071be066","observation_id":"3bd5f20b-76e8-4c7f-9442-db506106759e","resolution":{"observed_at":"2026-08-12T20:27:15.912616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.09779","last_updated":"2024-11-14T19:47:19Z","latest_version":1,"primary_category":"q-bio.PE","snapshot_observed_at":"2026-08-14T14:50:48.627203Z","submitted_at":"2024-11-14T19:47:19Z","title":"Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":1,"verified_fuzzy":24},"total_outbound_references":27},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2411.09779."}