{"as_of":"2026-08-20T11:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:65a28098b171c7d0e48ba550007342421dfa06a301795c98bef80f8e2043150f","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":"2026-08-11T21:37:01.064895Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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":"2026-08-15T19:52:29.585714Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T19:52:29.862894Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"cited_work":{"arxiv_id":"2412.04525","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.04525","snapshot_observed_at":"2026-08-15T19:52:29.862894Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","venue":"eess.IV","work_id":"0a7e9943-7e2f-47de-8aac-17e54dd5a9f1","year":2024},"citing_paper":{"arxiv_id":"2506.14719","last_updated":"2025-06-17T16:52:57Z","snapshot_observed_at":"2026-08-18T14:12:09.339205Z","submitted_at":"2025-06-17T16:52:57Z","title":"Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T19:52:29.585714Z"},"links":{"cited_paper":"/paper/2412.04525","citing_paper":"/paper/2506.14719"},"observation_digest":"sha256:475b4f325e77d2ba42948f92b2578a58a35208ba8b8ce0f7e3ef0ba798a88c43","observation_id":"21cf6ce3-bd66-4c5e-9b53-950f82f0cbfb","resolution":{"observed_at":"2026-08-15T19:52:29.869148Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.04525/citation-record","integrity":"/paper/2412.04525/integrity","json":"/paper/2412.04525/citation-record.json","paper":"/paper/2412.04525"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:37:01.739895Z","title":"A comprehensive review of deep learning-based single image super-resolution,","venue":null,"work_id":"67fc112f-4bc5-4e56-a73c-ac92aa55381e","year":2021},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.905929Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:d9d9c87694344f79efedd4b7d21aab5e582bd021cb1e081d32f76ff692fcc86f","observation_id":"c6c56659-cf07-48a1-81ff-60af636e61c9","resolution":{"observed_at":"2026-08-11T21:37:01.750843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09790","last_updated":"2026-06-16T05:30:45Z","snapshot_observed_at":"2026-08-19T22:23:58.992321Z","submitted_at":"2024-04-15T13:45:48Z","title":"NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09790","snapshot_observed_at":"2026-08-11T21:37:00.913075Z","title":"NTIRE 2024 challenge on image super- resolution: Methods and results,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.913075Z"},"links":{"cited_paper":"/paper/2404.09790","citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:4eb7cbdd2e1bc893286b006e846a371610955f61f4e5c498c23764cd8ed12779","observation_id":"964859ee-e2f2-4d99-9d1f-22ce5bfd0b6a","resolution":{"observed_at":"2026-08-11T21:37:00.913075Z","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":"2026-08-11T21:37:01.708399Z","title":"Beyond nyquist: A comparative analysis of 3d deep learning models enhancing mri resolution,","venue":null,"work_id":"07928bd5-54cf-42ea-9b05-01f978f70d97","year":2024},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.919215Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:c18e83d7f9f845e7d2f9277425f6c887e2a2d1502ce5397fb26af111762fc884","observation_id":"7b04339e-b670-43d7-b17d-df072a889dbd","resolution":{"observed_at":"2026-08-11T21:37:01.715585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.680063Z","title":"Generating high- resolution CT slices from two image series using deep-learning-based resolution enhancement methods,","venue":null,"work_id":"d3e66e27-777b-46b8-a29b-f66f3c938fe4","year":2022},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.925277Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:b8f5a6510c5f59674c11114d330a747c8b909d26eb9d2128927dfd0953687f95","observation_id":"ec052695-a26a-46aa-a62f-1879cf4500fd","resolution":{"observed_at":"2026-08-11T21:37:01.688811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16792","last_updated":"2024-11-25T09:12:55Z","snapshot_observed_at":"2026-08-18T14:10:49.863597Z","submitted_at":"2024-11-25T09:12:55Z","title":"From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task","version":1},"cited_work":{"arxiv_id":"2411.16792","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.16792","snapshot_observed_at":"2026-08-11T21:37:01.211497Z","title":"From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task","venue":"cs.CV","work_id":"73007ec1-0354-4a59-ae1e-fcba13877373","year":2024},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.931139Z"},"links":{"cited_paper":"/paper/2411.16792","citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:b7cf4c2f61baa4c40a22f0bb781fca49815488ca54dad6b04f9a0415abba6875","observation_id":"7c6035af-91c5-45d1-bab1-be5d456493b3","resolution":{"observed_at":"2026-08-11T21:37:01.218915Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:00.936880Z","title":"Image super-resolution using deep convolutional networks,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.936880Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:5eedbc492518c217b08aa104efad47fd16d0684f352a51556b595c0bd5c29bf2","observation_id":"d9fb3e40-5f16-49ce-9696-2a52306d11b9","resolution":{"observed_at":"2026-08-11T21:37:00.936880Z","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":"2026-08-11T21:37:01.641878Z","title":"Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network","venue":null,"work_id":"af6ad6c3-53ed-4b95-ab04-8a7df2ca26e2","year":2016},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.942936Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:a8ab7aefdda11a2a574f7e8438f7ee4ac1481f6349cf4a0179f5f8062fbcb675","observation_id":"97e33c6c-3a1f-42f4-baa7-70e3d8ed8fb0","resolution":{"observed_at":"2026-08-11T21:37:01.647874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:00.947974Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.947974Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:5f2c1ad57f0957734e7e62070d0aed201b83c514d11ae3eee982078e245d466d","observation_id":"c5720205-a1ef-4803-9d1d-2799b1040035","resolution":{"observed_at":"2026-08-11T21:37:00.947974Z","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":"2026-08-11T21:37:01.605309Z","title":"Photo-realistic single image super-resolution using a generative adversarial network,","venue":null,"work_id":"a9052d21-fa4a-4886-a6a3-a3a07e82188d","year":2017},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.953741Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:7a34f6855a7f755b3fb7c4a3224126a3bb434ac3ffef00b09af8319504157916","observation_id":"efd3f27e-f707-42dc-9c46-0f55b7053860","resolution":{"observed_at":"2026-08-11T21:37:01.612144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.585515Z","title":"Enhanced deep residual networks for single image super-resolution,","venue":null,"work_id":"1094f944-a0f3-40b7-afa6-352c262edfbe","year":2017},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.960437Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:8bcd44524d536c69b359492a10547072ac4ad59285d1c8208668a01ecb4fa432","observation_id":"4787340a-c2d1-430d-912b-0e45c41a3ed8","resolution":{"observed_at":"2026-08-11T21:37:01.592450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-17T19:17:06.411141Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-11T21:37:00.966275Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.966275Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:5eeec44b4ef53f851b723501781047aff77c254480ac26314a9e64812aa40a9b","observation_id":"6c42247e-0863-4f47-b8b8-4293990e6aed","resolution":{"observed_at":"2026-08-11T21:37:00.966275Z","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":"2026-08-11T21:37:01.565851Z","title":"Esrgan: Enhanced super-resolution generative adversar- ial networks,","venue":null,"work_id":"4a037c9c-4c38-4975-adef-b84044378cd9","year":2018},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.971646Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:047c56ea7075b18566aee1e8aecb75bd1ee5e0c8b636b1bc1286ac9a470f1d6b","observation_id":"070a0880-e03c-4322-8230-3913708b75d0","resolution":{"observed_at":"2026-08-11T21:37:01.572205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.00734","last_updated":"2018-09-10T17:11:59Z","snapshot_observed_at":"2026-08-14T18:56:38.104749Z","submitted_at":"2018-07-02T15:11:23Z","title":"The relativistic discriminator: a key element missing from standard GAN","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.00734","snapshot_observed_at":"2026-08-11T21:37:00.977361Z","title":"The relativistic discriminator: a key element missing from standard gan,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.977361Z"},"links":{"cited_paper":"/paper/1807.00734","citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:3988a0aaceaf51de56f9cd9d5082f88a389a931f007e4613d4cb2c58b9b5beb0","observation_id":"4228e837-a2da-410f-8dd9-4ec8c92939ac","resolution":{"observed_at":"2026-08-11T21:37:00.977361Z","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":"2026-08-11T21:37:01.543627Z","title":"2.5D deep learning for CT image reconstruction using a multi-gpu implementation,","venue":null,"work_id":"5d365146-700d-4338-9a98-48a880a49abb","year":2018},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.983788Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:9289a6ab30be6a3d06365a4bb38ec3828cb60811825569d29fac11e6b025352b","observation_id":"f65c2d3c-2181-4a3d-b466-a9eb78544f51","resolution":{"observed_at":"2026-08-11T21:37:01.550051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.511487Z","title":"Enabling rapid X- ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction,","venue":null,"work_id":"ba04738b-f355-4772-80c0-21c3a195f70c","year":2023},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:00.989119Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:877cbc8cc31f6520afe1d1eae11443a8397efcf6af50be86ed671e1a369d986b","observation_id":"eff8e852-8747-415d-9a03-3deac2e4c138","resolution":{"observed_at":"2026-08-11T21:37:01.523197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.488317Z","title":"Simurgh: A framework for cad-driven deep learning based X-Ray CT reconstruction,","venue":null,"work_id":"73e23f7f-48b6-4116-aa39-92b13ddb654a","year":2022},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.000121Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:f7aa0056c415afea0b8785e81dece8b2335f1cf4eeaec234fbae4e5d25d81bb9","observation_id":"1c6e084e-1c84-47a1-a700-4d38d935feaa","resolution":{"observed_at":"2026-08-11T21:37:01.494532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.13399","last_updated":"2023-09-23T15:21:28Z","snapshot_observed_at":"2026-08-20T01:13:10.363863Z","submitted_at":"2023-09-23T15:21:28Z","title":"MBIR Training for a 2.5D DL network in X-ray CT","version":1},"cited_work":{"arxiv_id":"2309.13399","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.13399","snapshot_observed_at":"2026-08-11T21:37:01.117642Z","title":"MBIR Training for a 2.5D DL network in X-ray CT","venue":"eess.IV","work_id":"8db8ac8f-8dc5-4717-8986-c710ee510636","year":2023},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.006167Z"},"links":{"cited_paper":"/paper/2309.13399","citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:c25799269185f97f6208b81f31ad1244e67f980b8fb3a346473cb1c447a309b8","observation_id":"7498bb23-26cb-4d60-a77a-039b1024110b","resolution":{"observed_at":"2026-08-11T21:37:01.125765Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.466000Z","title":"Multi-slice fusion for sparse-view and limited-angle 4D CT reconstruc- tion,","venue":null,"work_id":"b2954d6e-5911-4f10-9b27-e70f6c700d03","year":2021},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.013014Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:b533beef34aa0f904788ed7388bec42b162e9bac1c79f09730668718a9928d92","observation_id":"b0449d0f-6f4e-4054-9bc3-308657573449","resolution":{"observed_at":"2026-08-11T21:37:01.472542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.447417Z","title":"LoDoInd: Introducing a benchmark low-dose industrial CT dataset and enhancing denoising with 2.5D deep learning techniques,","venue":null,"work_id":"78a7719f-6b45-4800-9ee0-470638c70ef6","year":2024},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.019469Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:0b8f830262793a43cfa84fdff9154d65c847907bf3c5dacb63938c39bcbb997b","observation_id":"9904e77f-3094-4972-aac8-2b53dbb2ab46","resolution":{"observed_at":"2026-08-11T21:37:01.453950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.427673Z","title":"Deep learning based workflow for accelerated industrial X-ray computed tomography,","venue":null,"work_id":"3baaa6c4-17e0-4879-8027-b4dd326d513b","year":2023},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.025089Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:0d0de4a56d098cb723709c2260312386482d959b56210cde0e4723e0d0f34d53","observation_id":"d4e11145-da65-406f-ba92-a74264112c9d","resolution":{"observed_at":"2026-08-11T21:37:01.433880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.404318Z","title":"Bridging 2D and 3D seg- mentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions,","venue":null,"work_id":"82bee046-4ad3-45b1-8b00-8a910557cb38","year":2022},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.031222Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:dd2af1c5500262fbd868bee4f3215332d2a7eabab55e63161fdf4b1c5059e378","observation_id":"9bb6cb63-d90b-4043-b597-c3423ed4a7fd","resolution":{"observed_at":"2026-08-11T21:37:01.413222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.385789Z","title":"Spekpy v2. 0—a software toolkit for modeling x-ray tube spectra,","venue":null,"work_id":"7c0cdae8-c9d9-44a5-8750-d7a2e4ba0e54","year":2021},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.038490Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:e394d72cdbf07fb3ee94941407672a261860da44138d5ba2d3a2d82708c738a9","observation_id":"53b6ee11-ac14-48e9-ad5a-06e8e0f0bf4a","resolution":{"observed_at":"2026-08-11T21:37:01.391394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.365213Z","title":"A validation of spekpy: A software toolkit for modelling x-ray tube spectra,","venue":null,"work_id":"bf976250-3068-4683-926a-3dce6d924da5","year":2020},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.044108Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:b5ce1163d39b4d118d33dd788802c79d0f8205ffe7a1b26aa36a1ff2a6251371","observation_id":"3eba195a-e5b1-4752-bee4-e9a42bbe50bd","resolution":{"observed_at":"2026-08-11T21:37:01.370623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.344127Z","title":"Practical cone-beam algorithm,","venue":null,"work_id":"c8690c8b-3f77-438f-9a04-87102e224a67","year":1984},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.049829Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:5a2242e3b2cdd440744c453af2b0270e739b1ba5881a5fb58a489a53d6629776","observation_id":"aaa7a504-b942-4933-9db6-4eed07ca2938","resolution":{"observed_at":"2026-08-11T21:37:01.351276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.311648Z","title":"Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization,","venue":null,"work_id":"525789e5-f6c1-44e2-9084-17743eab998d","year":2010},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.054824Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:3576294da5cacfbaa013b80ef3b08f2cae3f389952eaaac0ce85d08dd66656a4","observation_id":"856ef3a9-630b-40d7-827e-11f4a5f8eee4","resolution":{"observed_at":"2026-08-11T21:37:01.326334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.284507Z","title":"Direct iterative reconstruction of multiple basis material images in photon-counting spectral CT,","venue":null,"work_id":"86febb57-1cef-4215-8904-8c438f58f8a2","year":2020},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.060016Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:419c36c510a530e38a49a51eda7c75a0a62526092de3c4a390053e7819ff00fa","observation_id":"f525c1d7-8d70-419a-9c9c-9f6448afadc9","resolution":{"observed_at":"2026-08-11T21:37:01.291276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:37:01.260542Z","title":"Neural Network-based Single-material Beam Hardening Correction for X-ray CT in Addi- tive Manufacturing ,","venue":null,"work_id":"fcfc6655-6ed9-45f4-ba02-3dfc87298ef4","year":2023},"citing_paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:01.064895Z"},"links":{"citing_paper":"/paper/2412.04525"},"observation_digest":"sha256:3a48d2039cad209cc3c2b3963191c1ba9862dd0a296d7fbcce1461248c77fc2b","observation_id":"1da9fd69-199f-4771-8abf-d576fdef8cc6","resolution":{"observed_at":"2026-08-11T21:37:01.267287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.04525","last_updated":"2024-12-05T15:56:52Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-18T14:11:31.191450Z","submitted_at":"2024-12-05T15:56:52Z","title":"2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":2,"verified_fuzzy":20},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.04525."}