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Paper Citation Record · LEDGER

Machine learning approach for mapping the stable orbits around planets

As of 22 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2412.04568.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.04568 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:04:57.096817Z

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation 352f0128-878a-48d1-a7d3-e9d6ca0b9962 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Machine learning approach for mapping the stable orbits around planets , " * write output.state after.block = add.period write newline

Reference 1

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This paper cites write newline.

Machine learning approach for mapping the stable orbits around planets write newline

Reference 2

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Observation 87a3eb9a-aad7-4e6d-ad9f-391577f15694 · outbound

This paper cites 2021, Artif.

Machine learning approach for mapping the stable orbits around planets 2021, Artif

Reference 3

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This paper cites 1996, Mach.

Machine learning approach for mapping the stable orbits around planets 1996, Mach

Reference 4

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This paper cites 2001, Mach.

Machine learning approach for mapping the stable orbits around planets 2001, Mach

Reference 5

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This paper cites 2023 a , FrASS, 10, 1196223.

Machine learning approach for mapping the stable orbits around planets 2023 a , FrASS, 10, 1196223

Reference 6

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Machine learning approach for mapping the stable orbits around planets 2023 b , FrASS, 10

Reference 7

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Observation d9134a91-5507-448b-ad71-0e1f7da6e646 · outbound

This paper cites V., Bowyer, K.

Machine learning approach for mapping the stable orbits around planets V., Bowyer, K

Reference 8

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This paper cites 2004, in IJCNN, Vol.

Machine learning approach for mapping the stable orbits around planets 2004, in IJCNN, Vol

Reference 9

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This paper cites & Guestrin, C.

Machine learning approach for mapping the stable orbits around planets & Guestrin, C

Reference 10

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This paper cites 2021, PNAS, 118, e2026053118.

Machine learning approach for mapping the stable orbits around planets 2021, PNAS, 118, e2026053118

Reference 11

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Observation 7a440726-2d0f-4f95-97fb-c5b13749b913 · outbound

This paper cites 2011, Random Forests.

Machine learning approach for mapping the stable orbits around planets 2011, Random Forests

Reference 12

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Observation 22b6e0ce-9011-4e0f-9f43-22f8a8b3a842 · outbound

This paper cites C., Winter, O.

Machine learning approach for mapping the stable orbits around planets C., Winter, O

Reference 13

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Machine learning approach for mapping the stable orbits around planets Unresolved cited work

Reference 14

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Machine learning approach for mapping the stable orbits around planets 2011, Stat

Reference 15

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This paper cites O'Reilly Media, Inc.

Machine learning approach for mapping the stable orbits around planets O'Reilly Media, Inc

Reference 16

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This paper cites 2005, in ICIC, Springer, 878--887.

Machine learning approach for mapping the stable orbits around planets 2005, in ICIC, Springer, 878--887

Reference 17

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Machine learning approach for mapping the stable orbits around planets A., & Li, S

Reference 18

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Machine learning approach for mapping the stable orbits around planets Unresolved cited work

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This paper cites 1967, MNRAS, 136, 245.

Machine learning approach for mapping the stable orbits around planets 1967, MNRAS, 136, 245

Reference 20

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Machine learning approach for mapping the stable orbits around planets M., & Semary, N

Reference 21

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Machine learning approach for mapping the stable orbits around planets 2017, Adv Neural Inf Process Syst, 30

Reference 22

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Machine learning approach for mapping the stable orbits around planets & Sheshadri, H

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Machine learning approach for mapping the stable orbits around planets & Kipping, D

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Machine learning approach for mapping the stable orbits around planets 1997, McGraw Hill

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Machine learning approach for mapping the stable orbits around planets 2020, in ICICS, IEEE, 243--248

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Machine learning approach for mapping the stable orbits around planets Unresolved cited work

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Machine learning approach for mapping the stable orbits around planets & Spiegel, D

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Machine learning approach for mapping the stable orbits around planets & Kenworthy, M

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Machine learning approach for mapping the stable orbits around planets 2011, A&A, 532, A79

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Machine learning approach for mapping the stable orbits around planets & Ben-David, S

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Machine learning approach for mapping the stable orbits around planets Unresolved cited work

Reference 34

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Machine learning approach for mapping the stable orbits around planets 2020, PNAS, 117, 18194

Reference 35

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Machine learning approach for mapping the stable orbits around planets 2016, ApJL, 832, L22

Reference 36

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Machine learning approach for mapping the stable orbits around planets & Glynn, K

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Machine learning approach for mapping the stable orbits around planets J., Mueller, A

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:29:48.593685Z

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.

source=arxiv_source observed=2026-08-11T21:29:48.539610Z digest=sha256:9389d8f3f7952c4a8b6dcf401f3e66f7c1dfd0a1d4345b09e42beea06bdd04d5

Observation 5fb9f80f-72be-4a14-b55b-18434a4cbbfb · outbound

This paper cites 2015, Comput.

Machine learning approach for mapping the stable orbits around planets 2015, Comput

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:29:48.582600Z

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.

source=arxiv_source observed=2026-08-11T21:29:48.543097Z digest=sha256:98f4c459848a158e6887d3ef4b490aebff42e5c739240901bdcc12b178f7d25b

Observation 83ece2ed-e695-4e30-bf58-7e83da0d7e17 · outbound

This paper cites 2016, Big Data Res., 5, 2.

Machine learning approach for mapping the stable orbits around planets 2016, Big Data Res., 5, 2

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:29:48.571884Z

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.

source=arxiv_source observed=2026-08-11T21:29:48.546086Z digest=sha256:807fcd781486361e45e6b8a0accbb0bc75422230254a3a3d10b291e43dac26b6

Pith citing papers

Observation eda4cff5-3864-4e97-b6d6-c0c85b65d43f · inbound

Predicting the final states of binary-single scattering with machine learning cites this paper.

Predicting the final states of binary-single scattering with machine learning Machine learning approach for mapping the stable orbits around planets

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-01T20:04:57.096817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T20:04:57.096817Z digest=sha256:ab8f2864384beedae8b2fdfc59227dc3bc2b0d314e3ecd268872c47f5c173e00