Paper Citation Record · LEDGER

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport

As of 17 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2412.10945.

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

pith.paper-citation-record.v1
2412.10945 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:30:34.624329Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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External citation measurements

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Outbound references

Observation 39c1c81a-8c0d-401f-8fe3-d5d42c206170 · outbound

This paper cites Camps-Valls, D.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Camps-Valls, D

Reference 1

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Observation 5b70bf39-0d16-4c0a-a3e5-0b83a91d44df · outbound

This paper cites Predicting wind- driven spatial deposition through simulated color images using deep autoencoders,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Predicting wind- driven spatial deposition through simulated color images using deep autoencoders,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:30:35.230318Z

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Observation e333dd56-854a-4f58-abc6-2bf7aeeb5c01 · outbound

This paper cites Deep convolutional autoencoders as generic feature extractors in seismological applications,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Deep convolutional autoencoders as generic feature extractors in seismological applications,

Reference 3

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Observation 18a2b57d-d02c-48b8-aa95-d92e199c5170 · outbound

This paper cites Image super-resolution using deep convolutional networks,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Image super-resolution using deep convolutional networks,

Reference 4

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Observation ff6b0f25-f493-4766-91be-239535c34f65 · outbound

This paper cites Turbulence in Focus: Benchmarking Scaling Behavior of 3D V olumetric Super-Resolution with BLASTNet 2.0 Data,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Turbulence in Focus: Benchmarking Scaling Behavior of 3D V olumetric Super-Resolution with BLASTNet 2.0 Data,

Reference 5

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Observation 81c70543-1223-4c49-adcc-2a2e55f61e8c · outbound

This paper cites Exploring lstm- based prediction for radioactive plume atmospheric dispersion in nuclear power plant emergencies: A preliminary study,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Exploring lstm- based prediction for radioactive plume atmospheric dispersion in nuclear power plant emergencies: A preliminary study,

Reference 6

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Observation e2eeea9f-8a7b-4b1e-b37e-a5e024a2a408 · outbound

This paper cites Accelerating high-strain continuum- scale brittle fracture simulations with machine learning,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Accelerating high-strain continuum- scale brittle fracture simulations with machine learning,

Reference 7

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Observation 76e27279-7c44-469f-81a9-fb56bace53c3 · outbound

This paper cites Uncertainty bounds for multivariate machine learning predic- tions on high-strain brittle fracture,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Uncertainty bounds for multivariate machine learning predic- tions on high-strain brittle fracture,

Reference 8

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Observation cf1bc0f2-a65b-4ccd-8c5e-9ad0b80468f1 · outbound

This paper cites Stressnet-deep learning to predict stress with fracture propagation in brittle materials,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Stressnet-deep learning to predict stress with fracture propagation in brittle materials,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:30:35.079499Z

Source-reported events for the cited work

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Observation e4a43ddd-d547-4f76-84e3-ed73566d86c2 · outbound

This paper cites Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:30:34.714600Z

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Observation 440418db-337e-41ba-b58e-85156da5db25 · outbound

This paper cites A hybrid spatiotem- poral deep model based on cnn and lstm for air pollution prediction,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport A hybrid spatiotem- poral deep model based on cnn and lstm for air pollution prediction,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:30:35.055729Z

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Observation 83097f55-2973-4776-9c9a-16822af73829 · outbound

This paper cites Capturing plume behavior in complex terrain: an overview of the Nevada National Security Site Meteorological Experiment (METEX21),.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Capturing plume behavior in complex terrain: an overview of the Nevada National Security Site Meteorological Experiment (METEX21),

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:30:35.028548Z

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Observation 8c72aaca-428f-4287-be8d-e22df99b9d9f · outbound

This paper cites Real-time xenon sensor analysis report PNNL- 35939,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Real-time xenon sensor analysis report PNNL- 35939,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:30:35.003923Z

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Observation 59278db1-42f0-4fad-b3f1-6b16adbff24f · outbound

This paper cites Large Eddy Simulations of Turbulent and Buoyant Flows in Urban and Complex Terrain Areas Using the Aeolus Model,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Large Eddy Simulations of Turbulent and Buoyant Flows in Urban and Complex Terrain Areas Using the Aeolus Model,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:30:34.978025Z

Source-reported events for the cited work

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Observation 297d4e00-cdc6-47b0-9417-f9f10d24e4a3 · outbound

This paper cites Large sample properties of simulations using Latin hypercube sampling,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Large sample properties of simulations using Latin hypercube sampling,

Reference 15

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Observation 8f4eac2e-75bd-4637-8cfc-d43123e57c4e · outbound

This paper cites On the use of symmetries in building surrogate models,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport On the use of symmetries in building surrogate models,

Reference 16

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Observation 2d8930f7-93f8-4f7d-903e-f70e11a1f4f0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Adam: A Method for Stochastic Optimization

Reference 17

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Observation 63ca5409-1343-4f92-948b-b56ee1970bb0 · outbound

This paper cites Goodfellow, Y.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Goodfellow, Y

Reference 18

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Observation 83fa5084-7781-49a4-a13e-27e23f8555a0 · outbound

This paper cites The distribution of the flora in the alpine zone. 1,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport The distribution of the flora in the alpine zone. 1,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:30:34.891337Z

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Observation 52d6ce36-dc28-472f-ae5b-82f8558dd9be · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Image quality assessment: from error visibility to structural similarity,

Reference 20

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A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Unresolved cited work

Reference 21

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Observation dfda96c2-17f9-49e9-baad-617a4aae2ba5 · outbound

This paper cites Its architecture comprises 3,214,401 trainable parameters.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Its architecture comprises 3,214,401 trainable parameters

Reference 22

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This paper cites • dec2: ConvTranspose3d(7 × 32, 7 × 16, 2, stride =.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport • dec2: ConvTranspose3d(7 × 32, 7 × 16, 2, stride =

Reference 23

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Observation af966332-c2a9-467e-8c2d-42e603fde6ee · outbound

This paper cites • dec3: ConvTranspose3d(7×16, 1, 2, stride = 2)followed by ReLU activation.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport • dec3: ConvTranspose3d(7×16, 1, 2, stride = 2)followed by ReLU activation

Reference 24

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Observation 225958ec-cae3-476c-9ac3-879c2ba20b06 · outbound

This paper cites Its architecture has 951,873 trainable parameters.

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Its architecture has 951,873 trainable parameters

Reference 25

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Observation 0d67a14a-6fc5-4fde-868a-01864effceca · outbound

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A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport Unresolved cited work

Reference 26

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