Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T10:40:30.014910Z
Paper Citation Record · LEDGER
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature
As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2411.19031.
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
34 of 34 outbound references displayed
- verified exact13
- verified fuzzy4
- unresolved13
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 63b6e137-e001-4ccb-b1e8-737e400e2e48 · outbound
This paper cites Nature Clim Change, 5, 107–113.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Nature Clim Change, 5, 107–113
Reference 12
- Resolution
- verified exact
- doi, observed 2026-08-12T10:40:30.231468Z
Source-reported events for the cited work
correction dated 2015-02-24. Source: crossref record 10.1038/nclimate2545->10.1038/nclimate2450:correction, observed 2026-07-11T03:11:39.353364+00:00. This notice travels one citation hop only.
Observation 978c49d1-f8cd-4dc7-8d81-7ea7b455cdad · outbound
This paper cites Nature Reviews Physics , 3(6), pp.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Nature Reviews Physics , 3(6), pp
Reference 15
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9c8e4b09-1ff4-4558-ab92-8a929a195e1f · outbound
This paper cites Nature Clim Change, 7, 885 –889.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Nature Clim Change, 7, 885 –889
Reference 17
- Resolution
- verified exact
- doi, observed 2026-08-12T10:40:30.170639Z
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 ef95e01a-bf50-4d70-8ce8-d8c6e86e734a · outbound
This paper cites ClimaX: A foundation model for weather and climate.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature ClimaX: A foundation model for weather and climate
Reference 18
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0039d319-a07f-44c8-8124-29a3e4db3023 · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 20
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81cd7b41-073f-4ef0-a461-b71205b1321a · outbound
This paper cites In: 2022 24th international conference on advanced communication technology (ICACT).
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature In: 2022 24th international conference on advanced communication technology (ICACT)
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation bc1b71f7-db9f-4fcc-891b-15fe20bea8a5 · outbound
This paper cites Journal of Computational Physics, 378, 686 –707.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Journal of Computational Physics, 378, 686 –707
Reference 22
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bab9fa2a-6818-49cc-8bea-848cbc4ffb41 · outbound
This paper cites Journal of Advances in Modeling Earth Systems,12, e2020MS002203.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Journal of Advances in Modeling Earth Systems,12, e2020MS002203
Reference 23
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ead979c8-4b6b-4886-8be9-b8c3ed9db08b · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 24
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 099df407-24c3-42a2-b6f1-d23e3c8ede80 · outbound
This paper cites In Proceedings of the 2015 international conference on advanced computer science and information systems, Depok, Indonesia, 10–11 October 2015; pp.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature In Proceedings of the 2015 international conference on advanced computer science and information systems, Depok, Indonesia, 10–11 October 2015; pp
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 34d8e958-db84-4804-94e3-783146f3f9df · outbound
This paper cites Geophysical Research Letters, 45, 12,616 – 12,622.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Geophysical Research Letters, 45, 12,616 – 12,622
Reference 26
- Resolution
- verified exact
- doi, observed 2026-08-12T10:40:30.125032Z
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 5edcfadf-f370-4d1f-8c7d-d707a45b0f37 · outbound
This paper cites Philosophical Transactions of the Royal Society A, 379(2194), 20200097.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Philosophical Transactions of the Royal Society A, 379(2194), 20200097
Reference 27
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 481c64e3-a718-4316-a024-099886ee95e7 · outbound
This paper cites Journal of Advances in Modeling Earth Systems, 11,2680 –2693.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Journal of Advances in Modeling Earth Systems, 11,2680 –2693
Reference 31
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b42b56ae-dae9-458b-ab62-1adc26721d86 · outbound
This paper cites IEEE Geosci.
Reference 34
- Resolution
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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 f018ff66-5520-41c9-8115-0cef51783757 · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 55
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d55be22f-f4ae-445a-808d-cbf1f3422c0e · outbound
This paper cites https://doi.org/10.1007/978-3-540-79881-1_2 Solomatine, D.P., Ostfeld, A.,.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature https://doi.org/10.1007/978-3-540-79881-1_2 Solomatine, D.P., Ostfeld, A.,
Reference 68
- Resolution
- verified exact
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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 97bbea5e-35dd-4a5c-af12-cf57dd8fdb79 · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 588
- Resolution
- verified exact
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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 7a797434-1a8a-461c-aa56-082cdef748b7 · outbound
This paper cites Analog forecasting of extreme - causing weather patterns using deep learning.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Analog forecasting of extreme - causing weather patterns using deep learning
Reference 1317
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2e3c3879-4321-4eb6-b1db-594d5c2a281b · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 1656
- Resolution
- verified exact
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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 e3f39edd-0264-4ca7-b4a5-651001a0aba1 · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 1993
- Resolution
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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 70109c67-0b6d-4068-aea9-7186aee93303 · outbound
This paper cites Neural Comput and Applic, 13, 112 –122.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Neural Comput and Applic, 13, 112 –122
Reference 2004
- Resolution
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Source-reported events for the cited work
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Observation defcab08-3ffe-4cde-92c7-ae4ef1f6693b · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 2008
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7197f8b-26f8-48e8-9890-ce2ed879fbd6 · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 2010
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f357aa62-27b3-4beb-99a4-e18964e0ae7b · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 2014
- Resolution
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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 c5657c5a-2fa7-49f9-9ea1-0d38cf1c51ee · outbound
This paper cites 379–386 Hao, P., Li, S., Song, J., Gao, Y .,.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature 379–386 Hao, P., Li, S., Song, J., Gao, Y .,
Reference 2015
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2ed2c5dd-68f8-445f-b9fb-147b4ebf3511 · outbound
This paper cites Remote Sens.
Reference 2017
- Resolution
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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 467f4680-270d-4608-8b98-4e45fa3ea5c4 · outbound
This paper cites Oceanography, 31, 162 –173.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Oceanography, 31, 162 –173
Reference 2018
- Resolution
- verified exact
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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 05041983-59c7-436b-bc27-0b1b6d27c863 · outbound
This paper cites Front Mar Sci 6:420.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Front Mar Sci 6:420
Reference 2019
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ed2fcf5-2c13-488f-b2b8-684e8834b569 · outbound
This paper cites IEEE Access 2020, 8, 180544–180557.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature IEEE Access 2020, 8, 180544–180557
Reference 2020
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 27108fb5-a17c-44d9-83a1-d9377aee6926 · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 2021
- Resolution
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Source-reported events for the cited work
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Observation cce6c69e-9c3b-4b29-a2f8-feecfeb38cd9 · outbound
This paper cites an unresolved cited work.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Unresolved cited work
Reference 2022
- Resolution
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Source-reported events for the cited work
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Observation 870676cc-c0de-4e1b-9074-7daa27a6cfba · outbound
This paper cites TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), Chiang Mai, Thailand, 495-500, https://doi.org/10.1109/TENCON58879.2023.10322451.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), Chiang Mai, Thailand, 495-500, https://doi.org/10.1109/TENCON58879.2023.10322451
Reference 2023
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d46acf7d-cff8-48e0-949c-71365a280456 · outbound
This paper cites A., El Amrani, C.,.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature A., El Amrani, C.,
Reference 3498
- Resolution
- verified exact
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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 de2a1fd8-3619-4a3d-8f1c-265b179cc95a · outbound
This paper cites F., Mimura, N.,.
Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature F., Mimura, N.,
Reference 4678
- Resolution
- verified exact
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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.
Pith citing papers
No inbound Pith citation observations are available.