Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T17:52:14.212662Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 100 of 155 outbound references and 1 inbound Pith citation observation for arXiv:2512.08090.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T17:52:14.212662Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-25T22:44:31.023366Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
100 of 155 outbound references displayed
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning , " * write output.state after.block = add.period write newline
Reference 1
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Observation e825a17b-3ac2-4ff7-ac03-ecf72aefc254 · outbound
Reference 2
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Observation 9cfccbcf-f3d9-4e2c-bf65-a88332287077 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Uncertainties in Galactic Chemical Evolution Models
Reference 3
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Observation 85e41c69-ef28-49a8-b394-218ad4acface · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning G., Navarro , J
Reference 4
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Observation 23ee0d45-1f96-457b-9ef5-a3e6e0aec9cc · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2003 b , , 597, 21, 10.1086/378316
Reference 5
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Observation 65f95e0f-4b05-40eb-a7d5-ecf4edb840f1 · outbound
Reference 6
Source-reported events for the cited work
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Observation c2d41c00-ba83-4f24-9784-d65625380ad1 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2010, Astronomy and Astrophysics, 519, A77, 10.1051/0004-6361/201014353
Reference 8
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Observation 2def64f0-d826-44da-967e-6556eb819706 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning K., Matteucci , F., Origlia , L., & Rich , R
Reference 9
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Observation 23aa32b5-73e7-4296-815f-9277b7ee295a · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2018 b , , 56, 223, 10.1146/annurev-astro-081817-051826
Reference 11
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Observation 4fb9585e-bd55-47a5-85ae-b7fd4d97bf85 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2019, , 489, 1082, 10.1093/mnras/stz2158
Reference 12
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Observation ecb04d75-e451-4f13-a3ea-7b3f30d155cd · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning M., Cole , S., & Frenk , C
Reference 13
Source-reported events for the cited work
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Observation 549f8879-1afe-47bf-9c27-9978022c9d7d · outbound
Reference 14
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Observation 1f6a19a8-0ad2-430b-aa7b-799ef36af2a4 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2011, Astronomy and Astrophysics, 533, A134, 10.1051/0004-6361/201117059
Reference 15
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Observation 83dbdaa1-6c6b-47d8-afa4-5a50ea9f3df4 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2017, , 605, A89, 10.1051/0004-6361/201730560
Reference 16
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Observation 58fdaf53-f0f3-45a3-9081-3efa61aaf5d6 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2008, The Astronomical Journal, 136, 2846, 10.1088/0004-6256/136/6/2846
Reference 17
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Observation 98f3c7ce-03e8-4e1c-9783-3a3bd190adfe · outbound
Reference 18
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Observation 59929105-f038-4bf9-9a2d-1c52e4502c3d · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2002, Monthly Notices of the Royal Astronomical Society, 330, 591, 10.1046/j.1365-8711.2002.05116.x
Reference 19
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Observation d7291656-9e77-412c-b47a-cbc6c90702af · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Unresolved cited work
Reference 20
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Observation 22d416c9-21f6-4264-b839-f2bc4f8dfc24 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning C., Casertano , S., et al
Reference 21
Source-reported events for the cited work
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Observation 16b3d21d-e87f-4b76-95ff-4aba5c26d718 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2018, in IAU Symposium, Vol
Reference 22
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Observation e3c755a7-5c9e-44f9-a78f-887e34fbc514 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2003, , 115, 763, 10.1086/376392
Reference 23
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Observation cc956526-90bc-4dc5-9180-9bc749e20751 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning J., Serenelli , A
Reference 24
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Observation ecec44a9-314c-4faa-aa3d-568c47ccc320 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2022, The Astrophysical Journal, 934, 28, 10.3847/1538-4357/ac795c
Reference 25
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Observation f3f1b9b3-2273-40b4-a861-b0e1d1d1f4fa · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2001, , 554, 1044, 10.1086/321427
Reference 26
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2003, , 339, 63, 10.1046/j.1365-8711.2003.06154.x
Reference 27
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Observation 8da0f827-ae98-4996-bfb8-924278fdc023 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2013, The Astrophysical Journal, 764, 21, 10.1088/0004-637X/764/1/21
Reference 28
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Observation 342215ac-8397-4774-b1d1-5954ff818a54 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning C., Fragkoudi , F., Khoperskov , S., Di Matteo , P., & Combes , F
Reference 29
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Observation 194fcb57-00d0-42ca-9f87-87143b3b3898 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 1990, , 233, 82
Reference 30
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Observation c6b4df57-10f5-4d0a-92e2-5a8a11143724 · outbound
Reference 31
Source-reported events for the cited work
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Observation 0edf284c-4f94-4d40-a8e8-4f02f9c66f6e · outbound
Reference 32
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Observation 7e8adc12-d667-469a-b6af-2d97225b3322 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2019, The Astrophysical Journal, 887, 213, 10.3847/1538-4357/ab5a88
Reference 33
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Observation 75b4004f-c8d6-4bf2-904a-7753214af331 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning O., Johnson , J
Reference 34
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 1999 b , , 517, 103, 10.1086/307200
Reference 36
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Reference 37
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Observation 66ccdf69-81de-49a3-900b-f7dd6ac4b5b3 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning K., Sahlholdt , C
Reference 38
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Unresolved cited work
Reference 39
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2012, Journal of Machine Learning Research, 13, 2171
Reference 40
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Unresolved cited work
Reference 41
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2020, , 499, 1116, 10.1093/mnras/staa2866
Reference 42
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Reference 43
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Unresolved cited work
Reference 44
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Observation 30a61e64-195c-41db-b470-1eebde2e4b7c · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2023, in American Astronomical Society Meeting Abstracts, Vol
Reference 45
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2012, The Messenger, 147, 25
Reference 46
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning A., Rejkuba , M., Zoccali , M., et al
Reference 47
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2011, Astronomy and Astrophysics, 530, A54, 10.1051/0004-6361/201116548
Reference 48
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning LSST Cadence Optimization White Paper: The Definitive Map of the Galactic bulge
Reference 49
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Unresolved cited work
Reference 50
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Reference 51
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2012, , 548, A60, 10.1051/0004-6361/201219761
Reference 52
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Observation a0f3a47e-2cdd-4745-a65a-84930868927b · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning H., Johnson , J
Reference 53
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Reference 54
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning R., Bovy, J., Holtzman, J
Reference 55
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2011, Astronomy and Astrophysics, 534, A80, 10.1051/0004-6361/200913757
Reference 56
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Asteroseismology with the Roman Galactic Bulge Time-Domain Survey
Reference 57
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Observation d1716e6f-b36a-4e00-bda6-5646eb0f5288 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Particle transport based study of nucleation in a ferromagnetic three-state spin system with conservative dynamics
Reference 58
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Reference 59
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Reference 60
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning I., Marchetti, T., et al
Reference 61
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Reference 63
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning C., & De Los Reyes , M
Reference 64
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Reference 65
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning I., & Lugaro , M
Reference 66
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Reference 67
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Reference 68
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Reference 69
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Reference 70
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Reference 71
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Reference 72
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Reference 73
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Reference 74
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2020, , 497, 3557, 10.1093/mnras/staa2205
Reference 75
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning C., & Newman , J
Reference 76
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning 2018, , 237, 13, 10.3847/1538-4365/aacb24
Reference 77
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Reference 78
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Observation a573b2b0-8913-438a-9c66-81c93d2d47e0 · outbound
The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning Unresolved cited work
Reference 79
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning E., Horta, D., et al
Reference 80
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning J., van Dokkum , P
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Reference 102
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The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning L., Gilbert , K., Tollerud , E., et al
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Reference 104
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Evaluating the Sensitivity of the Age Inferences of Red Giant Stars to Machine Learning Methodology The Two-infall Model Revisited: Constraints on Milky Way Bulge Assembly from >30,000 Galactic Chemical Evolution Models and Machine Learning
Reference 79
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