Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T23:24:36.165071Z
Paper Citation Record · LEDGER
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
As of 22 August 2026, this Paper Citation Record lists 100 of 167 outbound references and 7 inbound Pith citation observations for arXiv:2412.02527.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T04:47:57.349838Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T22:23:59.927409Z
Reference resolution
100 of 167 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b1da2ea4-b557-48c8-b683-42d3ac8a817c · outbound
This paper cites Abazajian, Jennifer K.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Abazajian, Jennifer K
Reference 1
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ed7570c-8a15-4569-9014-18c3283b7003 · outbound
This paper cites Filiz Ak, Shadab Alam, Carlos Allende Prieto, Andrés Almeida, Friedrich Anders, Scott F.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Filiz Ak, Shadab Alam, Carlos Allende Prieto, Andrés Almeida, Friedrich Anders, Scott F
Reference 2
- Resolution
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Observation 92732334-f254-4fb3-a0d7-cc4064ac3378 · outbound
This paper cites Lupton, Nate B.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Lupton, Nate B
Reference 3
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Unavailable: canonical work link unavailable.
Observation 46aa2880-4c08-4b25-9474-271a879a85b0 · outbound
This paper cites Lupton, Nate B.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Lupton, Nate B
Reference 4
- Resolution
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Unavailable: canonical work link unavailable.
Observation 522cc426-cee5-4d08-9cd6-ab50ca5e615a · outbound
This paper cites Bah- call, Steven Bickerton, James Bosch, Kevin Bundy, Peter L.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Bah- call, Steven Bickerton, James Bosch, Kevin Bundy, Peter L
Reference 5
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4401a989-f887-43cb-803b-3c8a5e67d366 · outbound
This paper cites Albareti, Carlos Allende Prieto, F.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Albareti, Carlos Allende Prieto, F
Reference 6
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f88f8c57-7885-44ac-bd36-41346843609c · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 7
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 50f44c26-48b5-48f3-a951-549b352f5df6 · outbound
This paper cites Alves, Hiranya V.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Alves, Hiranya V
Reference 8
- Resolution
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Observation 2d35d8c2-5238-4c0f-8917-51e613ac6885 · outbound
This paper cites Amanullah, C.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Amanullah, C
Reference 9
- Resolution
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Observation d7b142d1-74dc-4e85-a3c5-135d138e178e · outbound
This paper cites Galaxy zoo - the galaxy challenge, 2013.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Galaxy zoo - the galaxy challenge, 2013
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation d8dcaaa2-6df6-44e2-bf23-fdcc99fffc04 · outbound
This paper cites Audenaert, J.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Audenaert, J
Reference 11
- Resolution
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Observation 769446eb-90b6-4f38-bfb2-c54a911d0f1a · outbound
This paper cites Audenaert and A.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Audenaert and A
Reference 12
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b57c806-71ad-4f2e-9ea8-657891505f9f · outbound
This paper cites Bagley, Steven L.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Bagley, Steven L
Reference 13
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Source-reported events for the cited work
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Observation eb65be18-ec27-4e36-b05d-aa9d23501018 · outbound
This paper cites Bagley, Nor Pirzkal, Steven L.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Bagley, Nor Pirzkal, Steven L
Reference 14
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a0e4877e-e08a-46c4-a3ab-3f35c945579f · outbound
This paper cites Bellm, Shrinivas R.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Bellm, Shrinivas R
Reference 15
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Observation b18c756a-a92d-4143-887d-61104eff2246 · outbound
This paper cites Bellm, Shrinivas R.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Bellm, Shrinivas R
Reference 16
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Observation 3b3a48c7-77b9-46be-ae3a-21fcc0480636 · outbound
This paper cites Betoule, R.
Reference 17
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Observation 2580c94a-cd8f-4ca9-89fa-78be8ef1976c · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 18
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Observation ad1b81bf-9df6-4a22-8042-6aa045fdb2c9 · outbound
This paper cites Blanton, Matthew A.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Blanton, Matthew A
Reference 19
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e02c06e8-8436-49b3-959c-853ae1804ee1 · outbound
This paper cites A vocado: Photometric classification of astronomical transients with gaussian process augmentation.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data A vocado: Photometric classification of astronomical transients with gaussian process augmentation
Reference 20
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation abb2e6c0-0a96-4353-b9db-3d28eea13bd1 · outbound
This paper cites Borucki, David Koch, Gibor Basri, Natalie Batalha, Timothy Brown, Dou- glas Caldwell, John Caldwell, Jørgen Christensen-Dalsgaard, William D.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Borucki, David Koch, Gibor Basri, Natalie Batalha, Timothy Brown, Dou- glas Caldwell, John Caldwell, Jørgen Christensen-Dalsgaard, William D
Reference 21
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70d0ea48-8caf-49a5-a0e4-ef46053966a0 · outbound
This paper cites Radio galaxy zoo EMU: towards a semantic radio galaxy morphology taxonomy.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Radio galaxy zoo EMU: towards a semantic radio galaxy morphology taxonomy
Reference 22
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a48c602f-8b6e-47e2-a111-c2830824a126 · outbound
This paper cites A New Task: Deriving Semantic Class Targets for the Physical Sciences.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data A New Task: Deriving Semantic Class Targets for the Physical Sciences
Reference 23
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Observation a728a92b-93fa-45b0-a7e9-9d750cc6cdec · outbound
This paper cites Brout, M.
Reference 24
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e8230fbc-1f87-403a-9364-121b2ca27fb8 · outbound
This paper cites Brout, M.
Reference 25
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Source-reported events for the cited work
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Observation bf98598a-fbbc-4722-8964-b94c2e5656b1 · outbound
This paper cites Brown, Alice A.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Brown, Alice A
Reference 27
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Observation b126debe-fda6-4930-bf0d-98d772fbbb19 · outbound
This paper cites Amarsi, Thomas Nordlander, Karin Lind, Sarah L.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Amarsi, Thomas Nordlander, Karin Lind, Sarah L
Reference 28
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Unavailable: canonical work link unavailable.
Observation d7c4468a-f2dc-4c5c-a270-54763ccb3042 · outbound
This paper cites Bershady, David R.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Bershady, David R
Reference 29
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5654d59b-c20f-4e94-873b-56c48a663d1c · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 30
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Observation 62b77a90-9814-44a4-80b5-e78d78540f11 · outbound
This paper cites Burhanudin and Justyn R.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Burhanudin and Justyn R
Reference 31
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Observation 6f4969d5-0678-42f1-8496-65690c3edf89 · outbound
This paper cites Burns, Emilie Parent, M.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Burns, Emilie Parent, M
Reference 32
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Source-reported events for the cited work
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Observation 6a9309db-9640-4f3e-8c7b-9000cb01e599 · outbound
This paper cites Caldwell, Peter Tenenbaum, Joseph D.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Caldwell, Peter Tenenbaum, Joseph D
Reference 33
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Observation 9a92f5c3-25cb-4433-ac40-c7ad3ea6443f · outbound
This paper cites Davis, Dan Scolnic, Khaled Said, Dillon Brout, Erik R.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Davis, Dan Scolnic, Khaled Said, Dillon Brout, Erik R
Reference 34
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Source-reported events for the cited work
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Observation b2ad72c1-daf8-45ad-ad4d-a78ac9bdd975 · outbound
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The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Andrews, José Sánchez-Gallego, Joel Brownstein, María Argudo-Fernández, Michael Blanton, Kevin Bundy, Amy Jones, Karen Masters, David R
Reference 35
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Observation 7fca441b-bad9-44fd-b064-3877909cd60e · outbound
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The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 36
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Observation eefbe2db-0344-4458-bff5-3f67b34076f1 · outbound
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The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
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Observation 388a67b0-75b6-47d0-a193-51977f37540a · outbound
This paper cites De Angeli, M.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data De Angeli, M
Reference 38
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Observation e9d46a34-4570-476a-b452-af05ebd7bec9 · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 39
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Observation 704b841b-94ca-401d-92b5-6dfca9e5d796 · outbound
This paper cites The Early Data Release of the Dark Energy Spectroscopic Instrument.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data The Early Data Release of the Dark Energy Spectroscopic Instrument
Reference 40
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Observation bcaff576-5929-4367-a577-2930243fd4bf · outbound
This paper cites The DESI Experiment Part I: Science,Targeting, and Survey Design.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data The DESI Experiment Part I: Science,Targeting, and Survey Design
Reference 41
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Observation f6426c3a-c94f-4659-ab24-b1676eca1b81 · outbound
This paper cites Overview of the desi legacy imaging surveys.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Overview of the desi legacy imaging surveys
Reference 42
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Observation 60010ef3-f3f7-4e2f-a243-ef9be1ef2f9c · outbound
This paper cites Schlegel, Dustin Lang, Robert Blum, Kaylan Burleigh, Xiaohui Fan, Joseph R.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Schlegel, Dustin Lang, Robert Blum, Kaylan Burleigh, Xiaohui Fan, Joseph R
Reference 43
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Observation 1c268900-4e52-4151-a430-d6c909d51cb9 · outbound
This paper cites Schlegel, Dustin Lang, Robert Blum, Kaylan Burleigh, Xiaohui Fan, Joseph R.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Schlegel, Dustin Lang, Robert Blum, Kaylan Burleigh, Xiaohui Fan, Joseph R
Reference 44
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Observation 70042103-3b99-4081-a95d-050b12762547 · outbound
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The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Willett, and Joni Dambre
Reference 45
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Observation e1d560f0-3fa4-4562-98ee-7a40e228b584 · outbound
This paper cites Dobryakov, K.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Dobryakov, K
Reference 46
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Observation 4476edc8-44f7-4552-a233-689ee4ca0fa8 · outbound
This paper cites Domínguez Sánchez, M.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Domínguez Sánchez, M
Reference 47
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Observation 796de7ff-945a-42b0-adb7-48f7a8c82833 · outbound
This paper cites Dunlop, Roberto G.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Dunlop, Roberto G
Reference 48
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Observation 2bd80929-36cc-4d72-89e4-4e5773364051 · outbound
This paper cites Eisenstein, David H.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Eisenstein, David H
Reference 49
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Observation b8566034-ae51-441f-b884-05639307782a · outbound
This paper cites Overview of the JWST Advanced Deep Extragalactic Survey (JADES).
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Overview of the JWST Advanced Deep Extragalactic Survey (JADES)
Reference 50
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Source-reported events for the cited work
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Observation a6329a76-d45a-4845-a4cf-b6b53dc06e27 · outbound
This paper cites Evans, Francis A.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Evans, Francis A
Reference 51
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Observation 686d890c-4c8e-44f7-bef3-49616cbc6029 · outbound
This paper cites Finkelstein, Micaela B.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Finkelstein, Micaela B
Reference 52
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Source-reported events for the cited work
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Observation f310bb3a-fb5c-42eb-8eb7-dd1bc0040fde · outbound
This paper cites Foley, Daniel Scolnic, Armin Rest, S.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Foley, Daniel Scolnic, Armin Rest, S
Reference 53
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Source-reported events for the cited work
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Observation b796aeda-6564-427f-a830-50c450f365ac · outbound
This paper cites Major TOM: Expandable Datasets for Earth Observation.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Major TOM: Expandable Datasets for Earth Observation
Reference 54
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Unavailable: canonical work link unavailable.
Observation 297ab5d1-8b7d-43ff-a2e7-87bb368f8371 · outbound
This paper cites Freedman, Barry F.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Freedman, Barry F
Reference 55
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Observation 98c9bafd-a94b-4d16-8d7a-ad00e703fed8 · outbound
This paper cites Fremling, A.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Fremling, A
Reference 56
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Unavailable: canonical work link unavailable.
Observation 82c9c399-de1e-4e1a-839b-0ab2d0c670ab · outbound
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Reference 57
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Observation f7da9e23-5b31-4c4a-bf83-d67d13d10af9 · outbound
This paper cites Recio-Blanco, G.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Recio-Blanco, G
Reference 58
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 391c6b13-11b3-4196-9b68-e3a2c456dea9 · outbound
This paper cites Vallenari, A.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Vallenari, A
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.953748Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c213317f-5e05-4d80-851d-11ab19403037 · outbound
This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.958578Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b265a9e-7f79-4478-9d67-a3145253f610 · outbound
This paper cites García Pérez, Carlos Allende Prieto, Jon A.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data García Pérez, Carlos Allende Prieto, Jon A
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.963856Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ccc6bac-dbfe-4466-a61d-180fd4545253 · outbound
This paper cites MOMENT: A Family of Open Time-series Foundation Models.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data MOMENT: A Family of Open Time-series Foundation Models
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.969151Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation efe7bf4f-84b0-4d98-b4ee-c240f2a6a4f1 · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.979177Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30350640-f257-45d3-8695-033e958159ab · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.983733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb39f8c3-4ea4-4923-b433-1c8bd05f6a2f · outbound
This paper cites Deep residual learning for image recognition.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Deep residual learning for image recognition
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.988943Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf2e7935-6024-46ad-b45c-61a515d9821d · outbound
This paper cites Hebbar and Craig O.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Hebbar and Craig O
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.993531Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dc90a104-7f6e-44dc-8d76-bd48b8bfd512 · outbound
This paper cites Kirshner, Tom Matheson, Maryam Modjaz, Armin Rest, W.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Kirshner, Tom Matheson, Maryam Modjaz, Armin Rest, W
Reference 68
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:35.998343Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26228948-9480-49e0-a548-9c681a86775e · outbound
This paper cites Kirshner, Armin Rest, Claire E.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Kirshner, Armin Rest, Claire E
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.003165Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1feafd73-1687-43a8-88bf-bbf05bb7af7f · outbound
This paper cites Friedman, Stephane Blondin, Peter Challis, Perry Berlind, Mike Calkins, Gil Esquerdo, Thomas Matheson, Maryam Modjaz, Armin Rest, and Robert P.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Friedman, Stephane Blondin, Peter Challis, Perry Berlind, Mike Calkins, Gil Esquerdo, Thomas Matheson, Maryam Modjaz, Armin Rest, and Robert P
Reference 70
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.007980Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9c273eba-fe49-45a5-a1ee-3a6838724bb5 · outbound
This paper cites Hložek, A.
Reference 71
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.012494Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f976263f-77dc-4697-a94d-e97e5fcaa653 · outbound
This paper cites Quick Look.
Reference 72
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.017317Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f1f327e-cf30-4c62-a8e2-a012e0066222 · outbound
This paper cites Huang, Andrew Vanderburg, Andras Pál, Lizhou Sha, Liang Yu, Willie Fong, Michael Fausnaugh, A vi Shporer, Natalia Guerrero, Roland Vanderspek, and George Ricker.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Huang, Andrew Vanderburg, Andras Pál, Lizhou Sha, Liang Yu, Willie Fong, Michael Fausnaugh, A vi Shporer, Natalia Guerrero, Roland Vanderspek, and George Ricker
Reference 73
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.021976Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e074154-1443-450f-9f44-5621308d6f0b · outbound
This paper cites Huang, Andrew Vanderburg, Andras Pál, Lizhou Sha, Liang Yu, Willie Fong, Michael Fausnaugh, A vi Shporer, Natalia Guerrero, Roland Vanderspek, and George Ricker.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Huang, Andrew Vanderburg, Andras Pál, Lizhou Sha, Liang Yu, Willie Fong, Michael Fausnaugh, A vi Shporer, Natalia Guerrero, Roland Vanderspek, and George Ricker
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.027128Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b1930be-595f-4b95-bdf1-e30709c45ce8 · outbound
This paper cites Densely connected convolutional networks.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Densely connected convolutional networks
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.031910Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 502b01de-47fa-41d7-828c-596925056b5a · outbound
This paper cites Huang, C.
Reference 76
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.036629Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 35e6df85-6e0d-41bf-896a-10d066ec64cd · outbound
This paper cites Huertas-Company, K.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Huertas-Company, K
Reference 77
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.041750Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e63dae7f-3f0a-402e-88e7-5e94c60d86f8 · outbound
This paper cites Huertas-Company and F.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Huertas-Company and F
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.046559Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c234850c-6e2c-4e3d-9212-6f38696ccf68 · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 79
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.051560Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f253ac5-edd0-4948-9871-92c1418e1f90 · outbound
This paper cites Jenkins, Joseph D.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Jenkins, Joseph D
Reference 80
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.056625Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4a5d9f4c-c88d-437a-bfce-97817a8143ef · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 81
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.061709Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 728e091b-7fe7-409a-b41b-973cc81fc970 · outbound
This paper cites an unresolved cited work.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Unresolved cited work
Reference 82
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.066967Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 84cfb935-58db-4d57-8159-64ee73489868 · outbound
This paper cites Frieman, Alexandre Glazov, Santiago González-Gaitán, Renée Hlozek, Saurabh Jha, Stephen Kuhlmann, Martin Kunz, Hubert Lampeitl, Ashish Mahabal, James Newling, Robert C.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Frieman, Alexandre Glazov, Santiago González-Gaitán, Renée Hlozek, Saurabh Jha, Stephen Kuhlmann, Martin Kunz, Hubert Lampeitl, Ashish Mahabal, James Newling, Robert C
Reference 83
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.071782Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6423d1aa-87d4-41e6-bf9b-28982a3c60d9 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Adam: A Method for Stochastic Optimization
Reference 84
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.076436Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1a082120-db8e-4910-b0e7-90fa107a8f2a · outbound
This paper cites Koch, William J.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Koch, William J
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.081434Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c169fee2-b467-4240-8d03-1dd7b9df82ad · outbound
This paper cites Burns, M.
Reference 86
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.086604Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 578a7bc6-0dc8-4b71-811f-cc6dfbc941e3 · outbound
This paper cites Quick-look Pipeline Lightcurves for 9.1 Million Stars Observed over the First Year of 34 the TESS Extended Mission.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Quick-look Pipeline Lightcurves for 9.1 Million Stars Observed over the First Year of 34 the TESS Extended Mission
Reference 87
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.091422Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3287985d-e539-4c71-bf5c-0d3356011181 · outbound
This paper cites Quick-look Pipeline Light Curves for 5.7 Million Stars Observed Over the Second Year of TESS’ First Extended Mission.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Quick-look Pipeline Light Curves for 5.7 Million Stars Observed Over the Second Year of TESS’ First Extended Mission
Reference 88
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.096239Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77eed771-9c5a-4728-92a0-fc2c0531c490 · outbound
This paper cites Law, Brian Cherinka, Renbin Yan, Brett H.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Law, Brian Cherinka, Renbin Yan, Brett H
Reference 89
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.100912Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0e8de52-ae68-4a1d-a963-c613a9951144 · outbound
This paper cites Lee, Ralf Gommers, Filip Waselewski, Kai Wohlfahrt, and Aaron O’Leary.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Lee, Ralf Gommers, Filip Waselewski, Kai Wohlfahrt, and Aaron O’Leary
Reference 90
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.106638Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2671c1e7-45ac-40aa-ae32-0a6e16bc962e · outbound
This paper cites Leoni, E.
Reference 91
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.111897Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9b6e189c-f1bb-4711-a35f-ed1438c997fa · outbound
This paper cites Galaxy10 decals dataset.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Galaxy10 decals dataset
Reference 92
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.116570Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3244e109-74a9-4cd7-b6c0-13a79009557e · outbound
This paper cites Deep learning of multi-element abundances from high- resolution spectroscopic data.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Deep learning of multi-element abundances from high- resolution spectroscopic data
Reference 93
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.121372Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26c2b3af-ff74-4e5d-83f1-b6446498141a · outbound
This paper cites Leung and Jo Bovy.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Leung and Jo Bovy
Reference 94
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.126239Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31c155b8-83e6-4931-a150-8cd42c9e16a0 · outbound
This paper cites Learnable fourier features for multi-dimensional spatial positional encoding.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Learnable fourier features for multi-dimensional spatial positional encoding
Reference 95
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.131051Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c62ebb10-17ae-4aeb-a7fa-34f421aa6089 · outbound
This paper cites Outlier Detection in the DESI Bright Galaxy Survey.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Outlier Detection in the DESI Bright Galaxy Survey
Reference 96
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.136250Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f55ec33b-692e-4395-9def-dad655b75c8f · outbound
This paper cites Nichol, M.
Reference 97
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:24:36.141069Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e369d3b3-ad77-4dcb-8240-86076b1865d9 · outbound
This paper cites Galaxy zoo 1: data release of morphological classifications for nearly 900 000 galaxies.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Galaxy zoo 1: data release of morphological classifications for nearly 900 000 galaxies
Reference 98
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T23:24:38.021898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8c029745-f7f3-41d6-97a9-d1e50519c3f5 · outbound
This paper cites Lintott, Kevin Schawinski, Anže Slosar, Kate Land, Steven Bamford, Daniel Thomas, M.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Lintott, Kevin Schawinski, Anže Slosar, Kate Land, Steven Bamford, Daniel Thomas, M
Reference 99
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T23:24:38.006118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b76a1701-e829-4fce-ad67-55084b74eaf4 · outbound
This paper cites Allam, Tarek, Rahul Biswas, Johnny Holland, Ofer Lahav, Robert Schuhmann, Christian Setzer, and Max Winter.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Allam, Tarek, Rahul Biswas, Johnny Holland, Ofer Lahav, Robert Schuhmann, Christian Setzer, and Max Winter
Reference 100
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T23:24:37.990466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 921ac086-36cf-46b2-a482-04411bcb3339 · outbound
This paper cites McEwen, Hiranya V.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data McEwen, Hiranya V
Reference 101
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T23:24:37.973457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a7fb221b-d1c6-4f98-a628-a07f816a7e67 · outbound
This paper cites Majewski, Ricardo P.
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data Majewski, Ricardo P
Reference 102
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T23:24:37.956388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
Observation a12d2472-83c0-4d2a-9d8b-37539d86f502 · inbound
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Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515 The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T04:47:57.349838Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d941a1c-cf49-4180-b44b-fc1bfc549f56 · inbound
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From stellar light to astrophysical insight: automating variable star research with machine learning The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
Reference 171
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:22:39.455522Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88461b21-1356-4b71-92de-f64c1726b876 · inbound
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Causal Foundation Models: Disentangling Physics from Instrument Properties The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:32:22.716816Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9bcf891a-c5b9-4a75-b92e-059ffa094fbc · inbound
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Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T23:05:33.283641Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93a7d488-dbb7-4cd8-a5d4-b3f72b497b4b · inbound
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Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:40:18.301859Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c41e6977-cc42-47c3-a1e4-99e6663221a4 · inbound
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Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:23:59.929114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a07b3d6d-1c0e-4684-976e-7c8b28fa2939 · inbound
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Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T09:25:42.186226Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.