Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:09:55.185943Z
Paper Citation Record · LEDGER
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning
As of 12 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2501.07599.
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 2026-08-12T06:34:41.77262+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
66 of 66 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5febf7bd-de44-486f-82ee-066531737fe8 · outbound
This paper cites & Debney, A.
Reference 1
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Observation 718fab97-bd98-4644-977e-b951857f3d3a · outbound
This paper cites Statistics of three-dimensional lagrangian turbulence.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Statistics of three-dimensional lagrangian turbulence
Reference 2
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 3
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This paper cites & Cohen, E.
Reference 4
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Observation 8b80ecfa-1a32-454b-b7e8-782b5aa1fe6a · outbound
This paper cites Superstatistics in high-energy physics: application to cosmic ray energy spectra and e+ e-annihilation.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Superstatistics in high-energy physics: application to cosmic ray energy spectra and e+ e-annihilation
Reference 5
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Reference 6
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Reference 7
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Reference 8
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Observation a7b5907a-91e8-44f3-8569-9af1adc8394e · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 9
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Observation 269538a7-6242-42e5-87ca-bec5433344d5 · outbound
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Reference 10
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Observation e8f0d65d-a616-401b-b348-9849288e662f · outbound
This paper cites Kappa distributions: Theory and applications in plasmas (Elsevier, 2017).
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Kappa distributions: Theory and applications in plasmas (Elsevier, 2017)
Reference 11
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Observation d9986bfe-c101-42f9-8586-56ef6753be61 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 12
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Observation b617eb24-107e-4749-878d-42fd3ff19445 · outbound
This paper cites & Willitsch, S.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning & Willitsch, S
Reference 13
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Source-reported events for the cited work
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Observation d115e228-63ef-4dda-b40e-965f4ceb59dd · outbound
This paper cites V., Seno, F., Metzler, R.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning V., Seno, F., Metzler, R
Reference 14
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Observation 625024ea-4040-4bf9-802c-ce63f6e6173f · outbound
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Reference 16
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Observation 882f72ed-9144-405c-8266-0abf258bf268 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning & Smolyanov, O
Reference 17
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 21
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Observation ddcbcc2f-a969-420a-9054-795d3c578890 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 22
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Reference 23
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Observation de4ff676-af81-4911-bb51-9554cd98339c · outbound
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Reference 24
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Observation 1649b21f-cbbd-40a2-931e-81c5016addda · outbound
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Reference 25
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Observation c3234192-6bcf-49b1-8e0d-ad80d149d83e · outbound
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Reference 26
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Observation 4d10b43e-824f-40e6-a22b-28ae984b6b46 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
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Observation b1e9d806-35a5-45f2-bf5c-f487cf8033f4 · outbound
This paper cites Deep Learning using Rectified Linear Units (ReLU).
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Deep Learning using Rectified Linear Units (ReLU)
Reference 28
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Reference 29
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Observation 9ff77589-9a92-485a-a593-acae8d1b6605 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 30
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Reference 32
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Observation af45086c-d695-493f-8b96-76207aa788be · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
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Observation b7c33f16-70fe-4c36-b026-cff4edbd098c · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning & Garc ´ ıa, ´A
Reference 35
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Observation 7caec230-3df2-4d5d-9344-0dd8934b4f6d · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning &´Alvaro L´ opez Garc ´ ıa
Reference 36
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Observation 166b00dd-8240-4eec-b943-dbd8a0ba5887 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning J., Dominato, K
Reference 37
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Observation 19106764-b043-464d-96e0-96e6c4672821 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning & Hinkelmann, R
Reference 38
- Resolution
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Source-reported events for the cited work
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Observation 26ed59cd-f4ec-4039-b1a3-3a214fe2e42f · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 39
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Source-reported events for the cited work
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Observation de395784-c785-4582-8852-418e5f35f0f4 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 40
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Source-reported events for the cited work
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Superstatistics in hydrodynamic turbulence
Reference 41
- Resolution
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Observation a7e2a9bc-da93-4870-8c6e-b909b03669f7 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Water quality monitoring systems & services
Reference 42
- Resolution
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Observation 76e6e953-304c-477e-be56-d304ab4ea029 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Folium: Python data, leaflet.js maps
Reference 43
- Resolution
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Source-reported events for the cited work
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Observation a9fd90f0-d9e3-41c2-a190-44b8a84ba3b0 · outbound
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Reference 44
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Observation 1f393abb-cf35-4ac5-85d5-4b09d2741bf0 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Possible generalization of boltzmann-gibbs statistics
Reference 45
- Resolution
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Observation 7626888a-ed15-4524-981e-8a31be328706 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Dynamical foundations of nonextensive statistical mechanics
Reference 46
- Resolution
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Observation 8efa3b23-6daa-4f5f-a3fe-ddbb8eac6718 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 47
- Resolution
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Source-reported events for the cited work
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Observation bda9bd7f-623d-4914-b8c1-b9189b7a89d0 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 48
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 55de14ca-86b8-4c8a-9e08-849afaa87216 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Interpretable Machine Learning (Lulu
Reference 49
- Resolution
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Source-reported events for the cited work
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Observation 9a8e0f8a-d3d0-423f-a341-0e23c413d180 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 50
- Resolution
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Source-reported events for the cited work
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Observation d1a76baf-4e3a-4064-8473-4a13815351de · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning & Weinberger, K
Reference 51
- Resolution
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Source-reported events for the cited work
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Observation 79d2055e-50a8-480d-9021-4f3e3d24b824 · outbound
This paper cites An Introduction to Convolutional Neural Networks.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning An Introduction to Convolutional Neural Networks
Reference 52
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e03197df-6400-4765-ad57-919227a9e981 · outbound
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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning & Schmidhuber, J
Reference 53
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Source-reported events for the cited work
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Observation 7ae0bcc7-a96c-4a06-a8ea-d7c9f5abdeef · outbound
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Reference 54
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Source-reported events for the cited work
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Observation 3914d725-8709-4f66-85b5-2039da636bc8 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 55
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation d5f4bc53-359f-480c-9a2c-a6fe40485c86 · outbound
This paper cites & Matteson, D.
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:09:55.557940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation c45bd3e9-cbc3-4fbc-8341-3a078a81b850 · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 57
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:09:55.547791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8118b70e-d383-4947-88a5-35654df2a6aa · outbound
This paper cites Midas: Uk daily rainfall data.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Midas: Uk daily rainfall data
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:09:55.537523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 399f610b-abec-4d65-924b-734ee91de842 · outbound
This paper cites Midas uk hourly rainfall data.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Midas uk hourly rainfall data
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:09:55.525586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation df701a64-32ec-49c2-af63-805fe83f2aca · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 60
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T21:09:55.513849Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation d9564e32-9cfe-488a-a7e6-158ec6025e8d · outbound
This paper cites an unresolved cited work.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Unresolved cited work
Reference 61
- Resolution
- verified exact
- raw_fallback, observed 2026-08-10T21:09:55.349045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4b42c4a5-96f5-4516-b48a-919995b7ee30 · outbound
This paper cites https://www.statsmodels.org/stable/generated/statsmodels.tsa .seasonal.seasonal_decompose.html (2023).
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning https://www.statsmodels.org/stable/generated/statsmodels.tsa .seasonal.seasonal_decompose.html (2023)
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:09:55.502495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation d73d6804-b3b8-4e1e-8715-453954131586 · outbound
This paper cites Python implementation of empirical mode decomposition algorithm.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Python implementation of empirical mode decomposition algorithm
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:09:55.491639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4b00fc71-187c-47bc-a2e2-fb37482de398 · outbound
This paper cites & Koyama, M.
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:09:55.480271Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation de68b7be-3ffa-4efc-a38d-b2e4a1c04857 · outbound
This paper cites & Haffner, P.
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:09:55.468457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 96009540-ac5e-4395-9da5-c4149a5c7517 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning Adam: A Method for Stochastic Optimization
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:09:55.185943Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.