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Paper Citation Record · LEDGER

Prototype-enhanced prediction in graph neural networks for climate applications

As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2504.17492.

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

pith.paper-citation-record.v1
2504.17492 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:40:58.283737Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

  • verified exact1
  • verified fuzzy12
  • unresolved8
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c6bf8879-b6cd-461f-afc1-82dc28c36392 · outbound

This paper cites A gentle introduction to deep learning for graphs.

Prototype-enhanced prediction in graph neural networks for climate applications A gentle introduction to deep learning for graphs

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f9afb72f-8cac-41ca-97c4-1cbe5f49c29e · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Prototype-enhanced prediction in graph neural networks for climate applications Relational inductive biases, deep learning, and graph networks

Reference 2

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no resolver link, observed 2026-08-16T10:40:58.218381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T10:40:58.218381Z digest=sha256:cc1dcaded8a440da892a7cca271f4dfae1f90ba5cbb627f179eb9d529a719130

Observation 9447bf3e-6bbf-4785-a781-9b92245b39bb · outbound

This paper cites DeepClouds.ai: Deep learning enabled computationally cheap direct numerical simulations.

Prototype-enhanced prediction in graph neural networks for climate applications DeepClouds.ai: Deep learning enabled computationally cheap direct numerical simulations

Reference 3

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verified exact
local_arxiv, observed 2026-08-16T10:40:58.345711Z

Source-reported events for the cited work

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Observation df660f26-c22e-4bd3-ac8c-23aa89d0facb · outbound

This paper cites A machine learning emulator for lagrangian particle dispersion model footprints: a case study using name.

Prototype-enhanced prediction in graph neural networks for climate applications A machine learning emulator for lagrangian particle dispersion model footprints: a case study using name

Reference 4

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raw_fallback, observed 2026-08-16T10:40:58.490534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 43711f78-4b53-42e9-b2a8-127a29a71a3c · outbound

This paper cites Accelerating ghg emissions inference: A lagrangian particle dispersion model emulator using graph neural networks.

Prototype-enhanced prediction in graph neural networks for climate applications Accelerating ghg emissions inference: A lagrangian particle dispersion model emulator using graph neural networks

Reference 5

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raw_fallback, observed 2026-08-16T10:40:58.480920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T10:40:58.230245Z digest=sha256:6b5d7989f45e33cbd384a3d52aa961205f2d8f4c407d630e748f7c3d06f88df6

Observation 92e3ae57-4d66-42f0-a17a-120236ac93db · outbound

This paper cites Footnet: Development of a machine learning emulator of atmospheric transport.

Prototype-enhanced prediction in graph neural networks for climate applications Footnet: Development of a machine learning emulator of atmospheric transport

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7ed30acd-443a-46f5-bd36-061656db74f9 · outbound

This paper cites Evaluation of lagrangian particle dispersion models with measurements from controlled tracer releases.

Prototype-enhanced prediction in graph neural networks for climate applications Evaluation of lagrangian particle dispersion models with measurements from controlled tracer releases

Reference 7

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raw_fallback, observed 2026-08-16T10:40:58.460689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 84eb7e21-c535-42b4-ae9a-a8548372893b · outbound

This paper cites Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane.

Prototype-enhanced prediction in graph neural networks for climate applications Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane

Reference 8

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raw_fallback, observed 2026-08-16T10:40:58.450901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8a1a6112-014e-481b-9ca3-c8f694d73a80 · outbound

This paper cites The uk met office's next-generation atmospheric dispersion model, name iii.

Prototype-enhanced prediction in graph neural networks for climate applications The uk met office's next-generation atmospheric dispersion model, name iii

Reference 9

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raw_fallback, observed 2026-08-16T10:40:58.440134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cca0d39c-d4d8-45ac-8755-cfb3d4c10831 · outbound

This paper cites Forecasting Global Weather with Graph Neural Networks.

Prototype-enhanced prediction in graph neural networks for climate applications Forecasting Global Weather with Graph Neural Networks

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 32770612-5563-4ecd-bed8-ffb0533c9ebd · outbound

This paper cites GraphCast: Learning skillful medium-range global weather forecasting.

Prototype-enhanced prediction in graph neural networks for climate applications GraphCast: Learning skillful medium-range global weather forecasting

Reference 11

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no resolver link, observed 2026-08-16T10:40:58.250586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f59d82cd-541c-400f-8278-2b9bcc56797d · outbound

This paper cites Atmospheric impacts of the oil and gas industry.

Prototype-enhanced prediction in graph neural networks for climate applications Atmospheric impacts of the oil and gas industry

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-16T10:40:58.430024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 787c9ba0-5a58-45d6-a77e-d6f5122cefbd · outbound

This paper cites A decade of gosat proxy satellite ch 4 observations.

Prototype-enhanced prediction in graph neural networks for climate applications A decade of gosat proxy satellite ch 4 observations

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cbecc1cc-3732-46f6-8daf-90dd2d66522a · outbound

This paper cites Learning Mesh-Based Simulation with Graph Networks.

Prototype-enhanced prediction in graph neural networks for climate applications Learning Mesh-Based Simulation with Graph Networks

Reference 14

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unresolved
no resolver link, observed 2026-08-16T10:40:58.260206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 39a5f328-38be-4ef7-97e5-4fe8c66927bc · outbound

This paper cites Optimizing intersection-over-union in deep neural networks for image segmentation.

Prototype-enhanced prediction in graph neural networks for climate applications Optimizing intersection-over-union in deep neural networks for image segmentation

Reference 15

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raw_fallback, observed 2026-08-16T10:40:58.409749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-16T10:40:58.263401Z digest=sha256:d973c92fbf3392aaa721df44f9f68f8ed4b418fd7594db59f32c87de83fbff34

Observation 5bb90e75-b64e-435f-94b5-de0209251d43 · outbound

This paper cites Quantifying sources of brazil's ch 4 emissions between 2010 and 2018 from satellite data.

Prototype-enhanced prediction in graph neural networks for climate applications Quantifying sources of brazil's ch 4 emissions between 2010 and 2018 from satellite data

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-16T10:40:58.399920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 186c00ea-8529-4880-88ee-fad49c2b7e74 · outbound

This paper cites Enhancing computational fluid dynamics with machine learning.

Prototype-enhanced prediction in graph neural networks for climate applications Enhancing computational fluid dynamics with machine learning

Reference 17

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Unavailable: canonical work link unavailable.

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Observation faa02dcd-d329-44c5-8bd3-5d0afa883186 · outbound

This paper cites write newline.

Prototype-enhanced prediction in graph neural networks for climate applications write newline

Reference 18

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Observation ff7b118c-6d3d-4360-93fe-ce9c890b1eb8 · outbound

This paper cites @esa (Ref.

Prototype-enhanced prediction in graph neural networks for climate applications @esa (Ref

Reference 19

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unresolved
no resolver link, observed 2026-08-16T10:40:58.276788Z

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Observation 8de1aaee-f23b-4be6-b502-037127e09004 · outbound

This paper cites an unresolved cited work.

Prototype-enhanced prediction in graph neural networks for climate applications Unresolved cited work

Reference 20

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Unavailable: canonical work link unavailable.

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Observation 16a3a5a7-9637-42d9-826b-1bfdcd18f7e2 · outbound

This paper cites Tackling Climate Change with Machine Learning.

Prototype-enhanced prediction in graph neural networks for climate applications Tackling Climate Change with Machine Learning

Reference 21

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Pith citing papers

No inbound Pith citation observations are available.