Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:56:06.799172Z
Paper Citation Record · LEDGER
Exploring Design Choices for Autoregressive Deep Learning Climate Models
As of 19 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.02506.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
23 of 23 outbound references displayed
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- unresolved16
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5993f5b2-699a-4172-87b3-2fde66ba0f5a · outbound
This paper cites Embed.: False Use Pos.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Embed.: False Use Pos
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation c945fb40-75fc-4f1e-a738-40e3b86ae5da · outbound
This paper cites Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation ac0284d5-f634-479d-ac84-3a05fd741c3e · outbound
This paper cites an unresolved cited work.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Unresolved cited work
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation 63bf6d97-0648-4398-bb70-321df2ade90f · outbound
This paper cites LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles.
Exploring Design Choices for Autoregressive Deep Learning Climate Models LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
Reference 7
- Resolution
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Source-reported events for the cited work
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Observation 3129e2e4-19bd-4059-b291-419d08b53ab5 · outbound
This paper cites For better readability, the y-axis is cut-off at 0.5 and the number of displayed runs out of 10 is shown on the x-axis.
Exploring Design Choices for Autoregressive Deep Learning Climate Models For better readability, the y-axis is cut-off at 0.5 and the number of displayed runs out of 10 is shown on the x-axis
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 145ad7a7-49c4-4215-b080-95b3a90eec46 · outbound
This paper cites Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R
Reference 9
- Resolution
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Source-reported events for the cited work
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Observation f45e262e-6f18-4151-a68f-f42553d183a2 · outbound
This paper cites an unresolved cited work.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Unresolved cited work
Reference 10
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d67665c0-a9cd-4ea5-8e8f-a7d19c96207b · outbound
This paper cites Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhat- tacharya, Andrew Stuart, and Anima Anandkumar.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhat- tacharya, Andrew Stuart, and Anima Anandkumar
Reference 11
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0b1f5c0-92b4-4f38-b93a-464d5c3554e3 · outbound
This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.
Exploring Design Choices for Autoregressive Deep Learning Climate Models FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Reference 13
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eda1c6a8-7d85-4fb3-872c-6651c3d9f284 · outbound
This paper cites 6 Published as a workshop paper at ”Tackling Climate Change with Machine Learning”, ICLR 2025 Sebastian Scher and Gabriele Messori.
Exploring Design Choices for Autoregressive Deep Learning Climate Models 6 Published as a workshop paper at ”Tackling Climate Change with Machine Learning”, ICLR 2025 Sebastian Scher and Gabriele Messori
Reference 16
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 36ea562d-4928-4c66-b742-e81f844f6f2e · outbound
This paper cites Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
Reference 18
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9be0829c-25db-460b-a4d7-841426fbe5cd · outbound
This paper cites ACE: A fast, skillful learned global atmospheric model for climate prediction.
Exploring Design Choices for Autoregressive Deep Learning Climate Models ACE: A fast, skillful learned global atmospheric model for climate prediction
Reference 19
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 746c1b6f-b4fb-442c-a29c-7b7bd120f2f7 · outbound
This paper cites an unresolved cited work.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Unresolved cited work
Reference 20
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a733b812-21b1-49e9-9ea2-e1bf6427418d · outbound
This paper cites For better readability, the y-axis is cut-off at 0.5 and the number of displayed runs out of 10 is shown on the x-axis.
Exploring Design Choices for Autoregressive Deep Learning Climate Models For better readability, the y-axis is cut-off at 0.5 and the number of displayed runs out of 10 is shown on the x-axis
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b1550982-cdee-49ef-9d53-40dec1e9fe1f · outbound
This paper cites LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles.
Exploring Design Choices for Autoregressive Deep Learning Climate Models LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
Reference 2016
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d3bce36-423d-41de-9c6f-abd4b9708dd4 · outbound
This paper cites Michael McCabe, Peter Harrington, Shashank Subramanian, and Jed Brown.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Michael McCabe, Peter Harrington, Shashank Subramanian, and Jed Brown
Reference 2017
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7973b994-3088-4c8e-8b2a-b916c1283194 · outbound
This paper cites Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
Reference 2019
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f2e78ace-5f60-481d-a5ee-025c27f765fd · outbound
This paper cites Meehl, Catherine A.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Meehl, Catherine A
Reference 2020
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation fa167e18-7f1e-4f89-8469-edd90ce7d97b · outbound
This paper cites an unresolved cited work.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Unresolved cited work
Reference 2021
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 40f78f6b-a5f0-435b-95fe-144313d73269 · outbound
This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.
Exploring Design Choices for Autoregressive Deep Learning Climate Models FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Reference 2022
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d344389-5c60-486f-aeb9-17a504dfeba0 · outbound
This paper cites Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, and Anima Anandkumar.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, and Anima Anandkumar
Reference 2023
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2906288f-dd05-45ed-8c47-7b0d6f51291a · outbound
This paper cites Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
Reference 2024
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 00b8c3b6-7efb-44c4-9f1c-9859019ae9e4 · outbound
This paper cites Stephan Rasp, Peter D.
Exploring Design Choices for Autoregressive Deep Learning Climate Models Stephan Rasp, Peter D
Reference 2025
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.