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 13 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.

pith.paper-citation-record.v1
2411.19031 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:40:30.014910Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

34 of 34 outbound references displayed

verified exact13
verified fuzzy4
unresolved13
parse uncertain0
malformed identifier3
metadata mismatch1

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: unresolved; no resolver link, observed 2026-08-12T10:40:29.934093Z

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 2026-08-13T06:32:02.005865+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; no resolver link, observed 2026-08-12T10:40:29.946804Z

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; no resolver link, observed 2026-08-12T10:40:29.955444Z

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; raw_fallback, observed 2026-08-12T10:40:30.632292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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; no resolver link, observed 2026-08-12T10:40:29.963611Z

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; no resolver link, observed 2026-08-12T10:40:29.967931Z

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; no resolver link, observed 2026-08-12T10:40:29.972249Z

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; raw_fallback, observed 2026-08-12T10:40:30.618905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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; no resolver link, observed 2026-08-12T10:40:29.984775Z

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: unresolved; no resolver link, observed 2026-08-12T10:40:30.001905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b42b56ae-dae9-458b-ab62-1adc26721d86 · outbound

This paper cites IEEE Geosci.

Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature IEEE Geosci

Reference 34

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T10:40:30.457219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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: unresolved; no resolver link, observed 2026-08-12T10:40:30.006182Z

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; doi, observed 2026-08-12T10:40:30.089443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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; doi, observed 2026-08-12T10:40:30.102519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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; no resolver link, observed 2026-08-12T10:40:29.885491Z

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; doi, observed 2026-08-12T10:40:30.257291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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: verified exact; doi, observed 2026-08-12T10:40:30.280443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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: malformed identifier; doi_truncated, observed 2026-08-12T10:40:30.183865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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: malformed identifier; no resolver link, observed 2026-08-12T10:40:29.997447Z

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: unresolved; no resolver link, observed 2026-08-12T10:40:29.902567Z

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: verified exact; doi, observed 2026-08-12T10:40:30.244497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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; raw_fallback, observed 2026-08-12T10:40:30.645178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2ed2c5dd-68f8-445f-b9fb-147b4ebf3511 · outbound

This paper cites Remote Sens.

Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature Remote Sens

Reference 2017

Resolution: verified exact; doi, observed 2026-08-12T10:40:30.294167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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; doi, observed 2026-08-12T10:40:30.218604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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: unresolved; no resolver link, observed 2026-08-12T10:40:29.951377Z

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; raw_fallback, observed 2026-08-12T10:40:30.659468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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: malformed identifier; no resolver link, observed 2026-08-12T10:40:29.876315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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: verified exact; doi, observed 2026-08-12T10:40:30.315332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T10:40:29.894347Z

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; doi, observed 2026-08-12T10:40:30.049498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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; doi, observed 2026-08-12T10:40:30.205726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.