{"as_of":"2026-08-15T19:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:25773cc721cd42e2ab856712eb86c4321417977ec91abaa01ec8d7b4171d573a","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T04:34:16.670454Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"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/2607.22512/citation-record","integrity":"/paper/2607.22512/integrity","json":"/paper/2607.22512/citation-record.json","paper":"/paper/2607.22512"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:13.856134Z","title":"rep., CEN, Brussels, Belgium (2011)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:13.856134Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:11a69dcbb27bf06508f4cd804d8e566ea8871b027b7b16794656b50ffea17bcc","observation_id":"0126c928-5f90-4678-b0fb-81eee2ff91dd","resolution":{"observed_at":"2026-08-01T04:34:13.856134Z","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":"2026-08-01T04:34:13.902403Z","title":null,"venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:13.902403Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:6b5a00f9249895125be75739d883767f61d3440bd70227c034f1701feebbf498","observation_id":"4a524fee-9a38-462d-9cf5-2837f2b15629","resolution":{"observed_at":"2026-08-01T04:34:13.902403Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:13.981199Z","title":"Tsivilis, G","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:13.981199Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:89ef5b1e59a65c03a0aad86c1b2fb0cb2c0980d1b44be8eb1a34ffc01e8f8192","observation_id":"dc847ee1-5b30-4833-b505-b91f83770dea","resolution":{"observed_at":"2026-08-01T04:34:13.981199Z","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":"10.1016/0032-5910(94)02964-p","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Powder Technology","work_id":"d63877aa-e118-4b4c-920f-f36bcf65fa85","year":1995},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.075816Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:bb2b137ee2f1881139c5dccfe29d70e115fff017273500cd21fc42611febe05e","observation_id":"9c6b783c-1db5-4d30-9d00-408068644484","resolution":{"observed_at":"2026-08-01T04:39:36.061316Z","resolver_source":"doi","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":"2026-08-03T17:38:21.156791+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:21.156791+00:00","source":"openalex_status_cache"},{"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:14.156388Z","title":"Abdul, C","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.156388Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:88a004ca37199bdc88b618991f0ee9d02774578992972667ff8c4afbe20741de","observation_id":"d8c4dd35-7add-4001-8e1c-33c01796fc28","resolution":{"observed_at":"2026-08-01T04:34:14.156388Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:14.217902Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.217902Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:655db2371c3221f5ced8737b51fee7531d779100d85e4834657639414d227b6c","observation_id":"6feaab6a-ca7a-405f-b86f-8f4e870d1316","resolution":{"observed_at":"2026-08-01T04:34:14.217902Z","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":"2026-08-01T04:34:14.309077Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.309077Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:d0af426551ffa64f3daab6f1ef632b0c7c247d1ccaac53d79a86ece01476c5c0","observation_id":"0b9182cc-66fd-45fb-84b4-fe89b909a56f","resolution":{"observed_at":"2026-08-01T04:34:14.309077Z","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":"2026-08-01T04:34:14.375218Z","title":"Ben Chaabene, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.375218Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:f64b42854821c807575f76dc81c74009996abde71c58df4e63022085b8fed5a4","observation_id":"1eaa8fc9-dbc1-48c6-b056-eda2f9ab26a1","resolution":{"observed_at":"2026-08-01T04:34:14.375218Z","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":"2026-08-01T04:34:14.463199Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.463199Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:be56b7faac297a8e9b59797ccd8b66c1b7cae400dda97bbfc5e8e9d6b10ccba4","observation_id":"fdaeb1c2-0ee8-4683-a4a2-3806b6a8a4af","resolution":{"observed_at":"2026-08-01T04:34:14.463199Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:14.550070Z","title":"rep., CEN, Brussels, Belgium (2013)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.550070Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:eaa830826f323176c641758a1d748e079f461093e32657d74d52a7a58b47aa73","observation_id":"f0a79d2b-2b8d-44a1-a300-c9ebc05e9998","resolution":{"observed_at":"2026-08-01T04:34:14.550070Z","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":"2026-08-01T04:34:14.697876Z","title":"Hanein, F","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.697876Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:30e7f723a553e9a51df8ed2a336f48f4ca5e25fa8f27fe0545b2a830e9c7fffb","observation_id":"d221dbfd-a106-42fc-bc46-d9d59fd81774","resolution":{"observed_at":"2026-08-01T04:34:14.697876Z","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":"2026-08-01T04:34:14.735600Z","title":null,"venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.735600Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:9cd13ae671d4f893f9d274f38a53094063619c8477857338ac5e78853e7053b0","observation_id":"55bca0f6-49fa-46b1-b431-5034ca3e01f2","resolution":{"observed_at":"2026-08-01T04:34:14.735600Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.cemconres","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:39:35.781857Z","title":"Gobbo, L","venue":null,"work_id":"3bc289ce-dce5-4a4e-b0e9-8c6727e4812c","year":2004},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.787370Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:c0993561112b26aa02ceb2a890723a99c200fb57e07b315bd2089489f2e6644c","observation_id":"db203aae-bada-4e1d-8aff-fbab91dff4e2","resolution":{"observed_at":"2026-08-01T04:39:35.908205Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:14.835782Z","title":"Ichikawa, S","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.835782Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:46a9fe795387b6050b967b6a194df1076135819a74b07938e0107a8fc9ac8b9d","observation_id":"70226260-6bb1-4c6b-9c2a-2845ecec25b6","resolution":{"observed_at":"2026-08-01T04:34:14.835782Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:14.897000Z","title":"URLhttps://qwen.ai/blog?id=qwen3.5","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.897000Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:54e7b0f56f3dfc0ed7b8305aae5e8da878e36290d8aed61acccf99169a64914e","observation_id":"8323dfc0-d139-4b25-8133-e3ea26d473d1","resolution":{"observed_at":"2026-08-01T04:34:14.897000Z","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":"2026-08-01T04:34:14.956346Z","title":"Rosin, E","venue":null,"work_id":null,"year":1933},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:14.956346Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:af785a368a857bc25ad940ad0b5f4ecbaf0eb30aacbf1bcec76c7159c2233b9d","observation_id":"2aa98893-99b8-4a07-8640-b428702a9295","resolution":{"observed_at":"2026-08-01T04:34:14.956346Z","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":"2026-08-01T04:34:15.067755Z","title":"Breiman, Random forests, Machine Learn- ing 45 (1) (2001) 5–32","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.067755Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:21ad7e96450b23422a2490e196e0dbe2b1e2c9458309acff876d49e7cf1a460c","observation_id":"f20fbcc6-e7ea-462d-965c-db9553cbef96","resolution":{"observed_at":"2026-08-01T04:34:15.067755Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:15.123626Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.123626Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:238fda829d6698862f7cd492b37f4608f133d9e487d4d6948cbc132ad4f3178a","observation_id":"c1a604a5-95cf-460f-a55f-52d50b60c955","resolution":{"observed_at":"2026-08-01T04:34:15.123626Z","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":"2026-08-01T04:34:15.181126Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.181126Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:b87aa06761b11fc8782215cb3ea2c099a22c225d1523ccbadb44b394dadd5d10","observation_id":"aa8cd0b9-5086-4836-a8f7-e16aef041b23","resolution":{"observed_at":"2026-08-01T04:34:15.181126Z","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":"2026-08-01T04:34:15.245557Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.245557Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:77b40f564ffe47b158c020d3848958a8cd5422b414dfd430d556a0716566982f","observation_id":"22015a91-736f-4720-8d6c-fff31799e31e","resolution":{"observed_at":"2026-08-01T04:34:15.245557Z","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":"2026-08-01T04:34:15.331854Z","title":"rep., CEN, Brussels, Belgium (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.331854Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:fb990f3036b0b3d75be4699f0342106d60209acf67c87b0e91d9eadcf56cad55","observation_id":"257eeb1a-9cbe-4637-967b-89f9323feee4","resolution":{"observed_at":"2026-08-01T04:34:15.331854Z","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":"10.1016/0008-8846(90)90030-2","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Cement and Concrete Research","work_id":"9e808318-ad26-42f5-9818-a03690848820","year":1990},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.419946Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:fc81d35ea4a12cf56f529f36d1098c4b5a182cdb6214c107965f66f8237b3d37","observation_id":"efb350c1-3322-4876-bfb0-b273ca3dbce9","resolution":{"observed_at":"2026-08-01T04:39:35.767345Z","resolver_source":"doi","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":"2026-08-03T17:38:21.297661+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:21.297661+00:00","source":"openalex_status_cache"},{"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:15.514526Z","title":"Frigione, S","venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.514526Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:3f1e0021459683bed5519d44419793465a2062b843b1db08fc86e5053d92e3bb","observation_id":"2021acfc-4e24-42a6-9b82-7540320522b3","resolution":{"observed_at":"2026-08-01T04:34:15.514526Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:15.604755Z","title":"Škvára, K","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.604755Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:93df6030e77eb2b1602c231c3d41df639c9bac523c3f30ec6062cb696310ab91","observation_id":"fafa329d-7a20-40ca-a853-888975b3b239","resolution":{"observed_at":"2026-08-01T04:34:15.604755Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:15.666801Z","title":null,"venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.666801Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:3f673cc3cb278dffcafb8bc87adb627f94a399d58c7dcd9e206d86a5d595b6e2","observation_id":"f5c3be5d-112b-42fd-8b36-fd4edf8469d0","resolution":{"observed_at":"2026-08-01T04:34:15.666801Z","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":"2026-08-01T04:34:15.704507Z","title":"Enders, R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.704507Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:368c08ec264b13f9b785cbfd8f7cb2d0268dad55c73a411ff7526646e0402614","observation_id":"af39ca6d-4997-4d14-9f35-b2a82ea1a01d","resolution":{"observed_at":"2026-08-01T04:34:15.704507Z","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":"10.1016/0008-8846(78)90056-x","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Jawed, J","venue":"Cement and Concrete Research","work_id":"58ea8a7b-6404-4ba8-bdb1-d15f97ac20a6","year":1978},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.759702Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:1c531a4d610a1e59010e0e51efe3440e9cdbf7600d9a40d3ff4234df17f633e2","observation_id":"ae9f52c7-daff-47b5-b294-b747b75dbfcb","resolution":{"observed_at":"2026-08-01T04:39:35.620850Z","resolver_source":"doi","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":"2026-08-03T17:38:21.377936+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:21.377936+00:00","source":"openalex_status_cache"},{"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:15.841471Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.841471Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:be301a77d191d0acd111369bd99fc85754c79ef74eaab4ce11f7f291db72a5b6","observation_id":"5bc663a8-0b6f-4875-9fbc-8cc90f0bd604","resolution":{"observed_at":"2026-08-01T04:34:15.841471Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:15.938753Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:15.938753Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:d321dac448a2c29c511684fa95023dee72d2dc991a61a7f6a6a73d227b975080","observation_id":"e912f0f2-897c-45f2-98b6-be57bfa16c66","resolution":{"observed_at":"2026-08-01T04:34:15.938753Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1155/2023/6697842","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Advances in Civil Engineering","work_id":"72e4ed14-54f8-40be-b670-24bcd674c30f","year":2023},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:16.024681Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:e0a2923e12a6489369b654961645b274ee8842551195cca9f5b190074eab6d24","observation_id":"b30d3378-4702-4e7b-9326-8d250cc06668","resolution":{"observed_at":"2026-08-01T04:39:35.525812Z","resolver_source":"doi","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":"2026-08-03T17:38:21.435993+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:21.435993+00:00","source":"openalex_status_cache"},{"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:16.087874Z","title":"Odler, R","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:16.087874Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:2ba960632d29fdce1dd53fccaaaf545b3cb2b315004c63f9114d517a33c7e3f8","observation_id":"1bbeaa0b-4548-4f48-a34e-d28b4645e961","resolution":{"observed_at":"2026-08-01T04:34:16.087874Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s0008-8846(97)00030-6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Samet, S","venue":"Cement and Concrete Research","work_id":"41a7fd6f-8cad-4841-bffc-76dce7068c5f","year":1997},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:16.162227Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:25cef60beee9a98549bdf2beb1e76ef39ea1b925e64292eecbf57ae678cd29c3","observation_id":"4d6bfd4b-5008-42ec-8996-3f814b16f407","resolution":{"observed_at":"2026-08-01T04:39:35.392927Z","resolver_source":"doi","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":"2026-08-03T17:38:21.542999+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:21.542999+00:00","source":"openalex_status_cache"},{"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:16.231054Z","title":"Lerch, The influence of gypsum on the hy- dration and properties of Portland cement pastes, Research Department Bulletin RX012, Portland Cement Association, Skokie, Illinois (1946)","venue":null,"work_id":null,"year":1946},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:16.231054Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:814be27d46d6fad502cac77f0f93b02e102b7aebce6dd5ca9d9f719d7fa9aff1","observation_id":"4c36bf74-6d5c-43a4-bbab-7204621d8bf0","resolution":{"observed_at":"2026-08-01T04:34:16.231054Z","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":"10.1016/j.cemconres.2009.07.019","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Pourchet, L","venue":"Cement and Concrete Research","work_id":"86e06134-2605-4ea1-a34f-c1e60dc1605f","year":2009},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:16.390107Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:c9f23e6bbfa76f2dfe892f397097dfe59f28764ddee37f34b581d8e17b4653eb","observation_id":"e8375710-9827-4bde-8bf7-d146d613bbce","resolution":{"observed_at":"2026-08-01T04:39:35.275932Z","resolver_source":"doi","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":"2026-08-03T17:38:21.610325+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:21.610325+00:00","source":"openalex_status_cache"},{"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:34:16.551564Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:16.551564Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:411b82754c246a9c562f772a9b15bce805e9677b5b4462b0093cada8dbcf3b5e","observation_id":"ce41ed2b-731e-4a86-b461-0dc207015bd3","resolution":{"observed_at":"2026-08-01T04:34:16.551564Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:34:16.670454Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T04:34:16.670454Z"},"links":{"citing_paper":"/paper/2607.22512"},"observation_digest":"sha256:e7e118a1bd6e08ea66adf6ea0e339e1c08ab9d5b570b55e2018b90085dc261ec","observation_id":"35acbb04-4200-4fb1-850b-da59cad62425","resolution":{"observed_at":"2026-08-01T04:34:16.670454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.22512","last_updated":"2026-07-24T17:32:41Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-01T04:34:12.912217Z","submitted_at":"2026-07-24T17:32:41Z","title":"Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":13,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":36},"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 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.22512."}