Paper Citation Record · LEDGER

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT

As of 4 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 3 inbound Pith citation observations for arXiv:2604.10094.

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

pith.paper-citation-record.v1
2604.10094 v2

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T16:32:26.465120Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T14:56:21.326799Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-30T15:24:50.097675Z

Reference resolution

63 of 63 outbound references displayed

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verified fuzzy0
unresolved63
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 382e3365-6623-4241-80f9-c2e38c298aa4 · outbound

This paper cites N., Kostis, H.-N., & Thorpe, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT N., Kostis, H.-N., & Thorpe, A

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:25.846854Z

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Observation d96b0b00-aa61-4bf7-8eb0-86b366dfff2e · outbound

This paper cites J., Razak, A., Yawanarajah, S., Lunt, M., Tse, E.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT J., Razak, A., Yawanarajah, S., Lunt, M., Tse, E

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:25.978008Z

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Observation 86d8c7ff-a0e0-4ab7-b6c1-e8e6a86b1aa6 · outbound

This paper cites (2025, December 5).

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT (2025, December 5)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.027766Z

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Observation 1c785606-901a-4e90-b665-f31a1c7764ec · outbound

This paper cites K., Cusworth, D.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT K., Cusworth, D

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.090742Z

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Observation cb414d44-33d7-42e3-83ce-b1dc337db87b · outbound

This paper cites K., Thorpe, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT K., Thorpe, A

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.199944Z

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Observation aee4b33c-6319-4a28-a565-6cddc78e9f83 · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 6

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Observation e291c741-522c-419a-a292-266136a4c1a7 · outbound

This paper cites G., Thorpe, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT G., Thorpe, A

Reference 7

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Observation aa4a30df-7e03-45d9-9db0-09b758cf18e5 · outbound

This paper cites M., Brodrick, P.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT M., Brodrick, P

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.290904Z

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Observation 80484dfa-a2dd-4c8a-9541-5cb0cbdbca7a · outbound

This paper cites A., Shephard, M.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT A., Shephard, M

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.294013Z

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Observation a44a7e0c-a5ad-4feb-9d4f-7025d7d6e72e · outbound

This paper cites W., Thorpe, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT W., Thorpe, A

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.296962Z

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Observation 8d66804a-068b-4dfe-8172-8ece2175379a · outbound

This paper cites H., Jacob, D.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT H., Jacob, D

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.299981Z

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Observation b9d4a6fd-5a7e-4a2a-b1d9-0d4639070e43 · outbound

This paper cites J., Orth-Lashley, B., Girard, M., France, J., Fisher, R.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT J., Orth-Lashley, B., Girard, M., France, J., Fisher, R

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.303036Z

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Observation efe50dea-4cd5-4037-be29-ce037d1ef3fd · outbound

This paper cites Cusworth, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Cusworth, A

Reference 13

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Observation 64274d3f-dd67-4d3d-827c-f872f602269a · outbound

This paper cites M., Thorpe, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT M., Thorpe, A

Reference 14

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Observation 3f982104-6164-4f64-a0bc-d95dc20baf66 · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 15

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Observation c8540a4a-3839-49cc-901c-86eaeb2a32e4 · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 16

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Observation 91bf8127-1654-4dfe-8bf6-97e8f61032ba · outbound

This paper cites (2023, August 30).

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT (2023, August 30)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.317775Z

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Observation d2574d22-0885-4c6e-9b52-34276fc0be8f · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 18

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Observation 14ca200e-80ff-4d20-84b6-4e6be314054a · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 19

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Observation 359eaf13-4e52-4f04-a31b-e649c7c4f812 · outbound

This paper cites L., Marshall, B.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT L., Marshall, B

Reference 20

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Observation 8f6bf3f8-6a38-4c87-b196-35aa481227da · outbound

This paper cites E., Rothman, L.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT E., Rothman, L

Reference 21

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Observation 81bf1603-b836-4126-b483-0ef7db8d0470 · outbound

This paper cites J., Irakulis-Loitxate, I., & Guanter, L.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT J., Irakulis-Loitxate, I., & Guanter, L

Reference 22

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Observation 628ede35-2be8-495e-9e2f-256d4b552fc3 · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 23

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Observation 288cf422-0350-4f68-a05e-04f3fcb907ef · outbound

This paper cites W., Jensen, D., Bender, H., Vinckier, Q., Xiang, C., Olson-Duvall, W., Lundeen, S., & Thompson, D.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT W., Jensen, D., Bender, H., Vinckier, Q., Xiang, C., Olson-Duvall, W., Lundeen, S., & Thompson, D

Reference 24

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Observation 4295dbd9-4b10-49fc-8e82-2c3b48577822 · outbound

This paper cites H., Varon, D.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT H., Varon, D

Reference 25

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Observation c73d77a2-5a9e-43ff-b35d-af875bc84561 · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 26

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Observation 3038e85b-9b76-4119-9a86-e4d0c9fc5acd · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 27

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 28

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 29

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Observation 3a188ede-7555-4581-9e03-9e566121ef5c · outbound

This paper cites J., Varon, D.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT J., Varon, D

Reference 30

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 31

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Observation 6841d4fe-b4b6-43ee-94bc-95e0de9a65cd · outbound

This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 32

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Observation c8800864-7eb1-45cd-866b-c520ee0d57bc · outbound

This paper cites Decoupled Weight Decay Regularization.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Decoupled Weight Decay Regularization

Reference 33

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 34

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 35

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 36

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 37

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 38

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

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This paper cites A., & Brandt, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT A., & Brandt, A

Reference 40

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 41

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 42

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 43

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This paper cites J., Maasakkers, J.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT J., Maasakkers, J

Reference 44

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This paper cites D., El Abbadi, S.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT D., El Abbadi, S

Reference 45

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 46

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

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Observation 2710d870-aeeb-44c2-bffc-66566ddf5fbd · outbound

This paper cites R., Green, R.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT R., Green, R

Reference 49

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This paper cites R., Thorpe, A.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT R., Thorpe, A

Reference 50

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Observation 070d1df1-371d-4011-b2e7-ac458e5e856d · outbound

This paper cites K., Frankenberg, C., & Roberts, D.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT K., Frankenberg, C., & Roberts, D

Reference 51

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This paper cites K., Green, R.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT K., Green, R

Reference 52

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Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT K., Roberts, D

Reference 53

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 54

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

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This paper cites J., Jacob, D.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT J., Jacob, D

Reference 57

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

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This paper cites R., Green, R.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT R., Green, R

Reference 59

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Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT standard atmosphere transmittances

Reference 60

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Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT We compute the theoretical spectral radiance of the Sun, modeled as a blackbody at T=5778 K using Planck's Law

Reference 61

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Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Slot linear projections

Reference 62

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This paper cites an unresolved cited work.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.461828Z

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Observation 76175ed1-92dd-45a6-8693-f73ab0e24aa9 · outbound

This paper cites The Pearson correlation coefficient is computed across overlapping pixels, requiring a correlation exceeding 0.97 for the candidates to be classified as identical.

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT The Pearson correlation coefficient is computed across overlapping pixels, requiring a correlation exceeding 0.97 for the candidates to be classified as identical

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:26.465120Z

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Pith citing papers

Observation 4b27a135-933d-45b7-ac48-9b0fa4eb8f25 · inbound

Fully Automatic Trace Gas Plume Detection cites this paper.

Fully Automatic Trace Gas Plume Detection Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-11T23:16:26.126914Z

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Observation 7ee567cf-3b7a-4d8b-ad95-8c8719b81a68 · inbound

Fully Automatic Trace Gas Plume Detection cites this paper.

Fully Automatic Trace Gas Plume Detection Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T14:56:21.326799Z

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Observation f6069010-25ed-4a87-a0bf-a7c01797ad33 · inbound

Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing cites this paper.

Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-06-30T15:24:50.098944Z

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