Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:43:31.988175Z
Paper Citation Record · LEDGER
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice
As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2608.11545.
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 2026-08-19T06:32:44.657259+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
31 of 31 outbound references displayed
- verified exact6
- verified fuzzy10
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2b9c75ef-1326-4fe1-b7dd-ffa109bc7006 · outbound
This paper cites top of atmosphere.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice top of atmosphere
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.565289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f1110798-b554-4edb-b0f6-d487c7c1d1df · outbound
This paper cites an unresolved cited work.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-16T00:43:32.842012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a84400f0-e43c-4b91-afe6-acbbe7734f07 · outbound
This paper cites AIMIP Phase 1: systematic evaluations of AI weather and climate models.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice AIMIP Phase 1: systematic evaluations of AI weather and climate models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.711791Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 616cfdae-5f8b-4f69-afd2-7ab656860b9d · outbound
This paper cites Daniel Holmberg, Emanuela Clementi, and Teemu Roos.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Daniel Holmberg, Emanuela Clementi, and Teemu Roos
Reference 7
- Resolution
- verified exact
- raw_fallback, observed 2026-08-16T00:43:32.398165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 554d03f7-09d5-4aea-a40a-16a90ee8c320 · outbound
This paper cites Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-08-16T00:43:32.217477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 93383bae-1dbc-4aac-8415-3cf9f16d7c34 · outbound
This paper cites doi:10.1029/2024JH000433.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice doi:10.1029/2024JH000433
Reference 10
- Resolution
- verified exact
- doi, observed 2026-08-16T00:43:32.070831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4cc6a3e8-1eb1-4e5e-ba65-744a13487fde · outbound
This paper cites Representing the Surface Ocean in ECMWF's data-driven forecasting system AIFS.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Representing the Surface Ocean in ECMWF's data-driven forecasting system AIFS
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.739146Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8890b26b-7e88-4e52-8a4c-797380a713b3 · outbound
This paper cites WeatherBench 2: A benchmark for the next generation of data-driven global weather models.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice WeatherBench 2: A benchmark for the next generation of data-driven global weather models
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.803584Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26c56ec6-ab2a-4fe4-add3-2dbee26594b9 · outbound
This paper cites Youmin Tang, Richard Kleeman, and Sonya Miller.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Youmin Tang, Richard Kleeman, and Sonya Miller
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.908536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a190c4e9-7298-416e-b37d-79eaccb0ac61 · outbound
This paper cites The 2023 global warming spike was driven by the el niño–southern oscillation.Atmospheric chemistry and physics, 24 (19):11275–11283,.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice The 2023 global warming spike was driven by the el niño–southern oscillation.Atmospheric chemistry and physics, 24 (19):11275–11283,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.658272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a2d625dc-518f-4a59-9f75-d88664d1356c · outbound
This paper cites an unresolved cited work.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Unresolved cited work
Reference 22
- Resolution
- verified exact
- doi, observed 2026-08-16T00:43:32.023047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1279d951-ed07-49de-8048-a5403dc3f52b · outbound
This paper cites Era5 hourly data on single levels from 1940 to present.Copernicus climate change service (c3s) climate data store (cds), 10(1.24381):24381,.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Era5 hourly data on single levels from 1940 to present.Copernicus climate change service (c3s) climate data store (cds), 10(1.24381):24381,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.578968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 917f9f1a-80eb-4992-a14c-102ef0dffb22 · outbound
This paper cites an unresolved cited work.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Unresolved cited work
Reference 27
- Resolution
- unresolved
- raw_fallback, observed 2026-08-16T00:43:32.550963Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 31ceff0d-818d-4b76-bef2-853c80fe816d · outbound
This paper cites Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel P shows the 50-member ensemble mean.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel P shows the 50-member ensemble mean
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.537619Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9f51a892-3d08-4607-bf5f-7ef6cf72deb2 · outbound
This paper cites Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel P shows the 50-member ensemble mean.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel P shows the 50-member ensemble mean
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.523786Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 28d9f40c-99f7-4f9a-9c37-9bf3e7d39644 · outbound
This paper cites Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel P shows the 50-member ensemble mean.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel P shows the 50-member ensemble mean
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.509301Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5c237c52-9f3e-4268-a533-2e05adb8f441 · outbound
This paper cites Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel L shows the 50-member ensemble mean.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Panel K shows the ensemble member with the lowest RMSE over the full Hovmöller with respect to UFS-Replay, and panel L shows the 50-member ensemble mean
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.495633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 799abfab-b229-488b-bcf0-30c4ed71bf0c · outbound
This paper cites FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Reference 2000
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.753318Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ccb596b-9275-4fd2-9090-bdd68432e220 · outbound
This paper cites Fair scores for ensemble forecasts.Quarterly Journal of the Royal Meteorological Society, 140(683): 1917–1923,.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Fair scores for ensemble forecasts.Quarterly Journal of the Royal Meteorological Society, 140(683): 1917–1923,
Reference 2001
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.681731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 63ebf13f-fb7a-4df8-b453-73aa932f5cdb · outbound
This paper cites Physical drivers of the summer 2019 north pacific marine heatwave.Nature communications, 11(1):1903,.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Physical drivers of the summer 2019 north pacific marine heatwave.Nature communications, 11(1):1903,
Reference 2003
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.605847Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d5a6fbae-6e8c-41c9-b960-dbdf366423a4 · outbound
This paper cites doi:10.1086/427976.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice doi:10.1086/427976
Reference 2005
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.776960Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 744a85d7-d021-4fe1-89b2-8e16b7be3e65 · outbound
This paper cites Eduardo Blanchard-Wrigglesworth, Roberto Bilbao, Aaron Donohoe, and Stefano Materia.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Eduardo Blanchard-Wrigglesworth, Roberto Bilbao, Aaron Donohoe, and Stefano Materia
Reference 2006
- Resolution
- verified exact
- doi, observed 2026-08-16T00:43:32.037593Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4842edf5-9555-40eb-8343-e3fcd1a4376a · outbound
This paper cites Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.748985Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63a1f739-de84-4544-aff8-a4e63a9d5f5c · outbound
This paper cites The era5 global reanalysis.Quarterly journal of the royal meteorological society, 146(730):1999–2049,.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice The era5 global reanalysis.Quarterly journal of the royal meteorological society, 146(730):1999–2049,
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.762816Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02a93388-36f8-4c37-a85b-4db5a3c2cc68 · outbound
This paper cites Forecasting Global Weather with Graph Neural Networks.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Forecasting Global Weather with Graph Neural Networks
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.692236Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7937ee56-0edd-42d6-8cae-638ed70cf54d · outbound
This paper cites Huge ensembles–part 1: Design of ensemble weather forecasts using spherical fourier neural operators.Geoscientific Model Development, 18(17):5575–5603, 2025a.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Huge ensembles–part 1: Design of ensemble weather forecasts using spherical fourier neural operators.Geoscientific Model Development, 18(17):5575–5603, 2025a
Reference 2020
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:43:32.592753Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 54c55372-cc5a-4e85-bec1-73469694af39 · outbound
This paper cites doi:10.1038/s41467-021-25257-4.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice doi:10.1038/s41467-021-25257-4
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.729872Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation edf51111-ac94-4353-a95a-67a23f67f3d3 · outbound
This paper cites doi:10.1029/2022GL102649.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice doi:10.1029/2022GL102649
Reference 2023
- Resolution
- verified exact
- doi, observed 2026-08-16T00:43:32.092334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9dc9a52b-5eee-46ec-a7ee-9a859927d16e · outbound
This paper cites Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.706959Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0e038fa-84ef-4e12-b5d0-04f17fb375e0 · outbound
This paper cites Atmospheric Predictability Beyond 30 Days with Machine Learning.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Atmospheric Predictability Beyond 30 Days with Machine Learning
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.697634Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06825c5b-c541-4a6e-bc20-cd44bafade37 · outbound
This paper cites Samudra 2: Scaling Ocean Emulators across Resolutions.
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice Samudra 2: Scaling Ocean Emulators across Resolutions
Reference 2026
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:43:31.744029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.