Paper Citation Record · LEDGER

Dynamic Modes as Time Representation for Spatiotemporal Forecasting

As of 14 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2506.01212.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.01212 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:55:03.363540Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:39:40.068846Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T05:39:43.053747Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy37
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0e603d9-73fa-4b3a-863f-09860a8ee9a1 · outbound

This paper cites Data-driven analysis and forecasting of highway traffic dynamics.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Data-driven analysis and forecasting of highway traffic dynamics

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:05.141687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e88ef9e5-80fe-4f4b-aef9-25b5db88391d · outbound

This paper cites Adaptive graph convolutional recurrent network for traffic forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Adaptive graph convolutional recurrent network for traffic forecasting

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:05.115626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 799f03bf-5df1-4364-8b7d-a32bf467f966 · outbound

This paper cites Pattern recognition and machine learning , volume 4.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Pattern recognition and machine learning , volume 4

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:02.417941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8b04754-f6ff-4aca-a122-70db0e3b429a · outbound

This paper cites Time series analysis: forecasting and control.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Time series analysis: forecasting and control

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:05.075533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2f64bd8c-5811-4e2a-b59b-d78b8579f7f5 · outbound

This paper cites Data-driven science and engineering: Machine learning, dynamical systems, and control.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Data-driven science and engineering: Machine learning, dynamical systems, and control

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:02.437707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84527af8-8854-433c-9597-c5449c387334 · outbound

This paper cites Extracting spatial--temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Extracting spatial--temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:05.027832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e787f3b0-4b09-4b5a-a612-56759521cdad · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.997059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c3454eda-dad5-4616-a830-8c9328aabbdf · outbound

This paper cites Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.956171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 65c85ad6-b144-4909-8f88-839e27736d04 · outbound

This paper cites Data-driven discovery of coordinates and governing equations.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Data-driven discovery of coordinates and governing equations

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.914541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9d28f9bb-bfd9-4dcb-8b68-e0254f75a3fe · outbound

This paper cites Discovery of nonlinear multiscale systems: Sampling strategies and embeddings.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Discovery of nonlinear multiscale systems: Sampling strategies and embeddings

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.882754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 909a9ef4-7f64-4ac9-a29d-8d29373d67fd · outbound

This paper cites Freeway performance measurement system: mining loop detector data.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Freeway performance measurement system: mining loop detector data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.834994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8c01f751-090a-42b1-8943-728f318796fd · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.789007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1d1c6167-5ae0-4ae7-9478-748b08794a82 · outbound

This paper cites Time series analysis: with applications in R , volume 2.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Time series analysis: with applications in R , volume 2

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.755430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd9e9389-b19c-4c36-a5e1-24de8f3ecf23 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Convolutional neural networks on graphs with fast localized spectral filtering

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.720329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 85b591db-c25b-4968-9200-d4ea5d0c6382 · outbound

This paper cites Long-Range Transformers for Dynamic Spatiotemporal Forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Long-Range Transformers for Dynamic Spatiotemporal Forecasting

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:02.590927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7501f40b-1cee-45ad-8c12-c74ada2e1803 · outbound

This paper cites Attention based spatial-temporal graph convolutional networks for traffic flow forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Attention based spatial-temporal graph convolutional networks for traffic flow forecasting

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:02.616589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2688285-b6a7-4190-b166-395865bac531 · outbound

This paper cites De-biasing the dynamic mode decomposition for applied koopman spectral analysis of noisy datasets.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting De-biasing the dynamic mode decomposition for applied koopman spectral analysis of noisy datasets

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.677589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 681bccd3-784a-4fcc-8a59-6cb3ff30d9bc · outbound

This paper cites Long Short-Term Memory.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Long Short-Term Memory

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.645162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6e3a3494-6ee6-4d98-a5c2-187a894f7d02 · outbound

This paper cites Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.604058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 971017a4-e4b0-4a3e-8f86-eaf330143433 · outbound

This paper cites Sparsity-promoting dynamic mode decomposition.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Sparsity-promoting dynamic mode decomposition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.563733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f2d42b54-16cf-4e68-aa21-5c6cf072a13b · outbound

This paper cites Time-delay observables for koopman: Theory and applications.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Time-delay observables for koopman: Theory and applications

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.512780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b68a490d-a43e-4d32-a2e5-4480a72a2a6a · outbound

This paper cites Statistical methods versus neural networks in transportation research: Differences, similarities and some insights.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.463819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5e41d33a-a1a4-4d29-a46d-73bc6ed73fa4 · outbound

This paper cites Time2Vec: Learning a Vector Representation of Time.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Time2Vec: Learning a Vector Representation of Time

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:02.781724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a285a315-0399-4fba-9198-93a6671dd10d · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Semi-supervised classification with graph convolutional networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.411647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af20fc7d-c54b-45a8-a0e0-c762ddaf2b4e · outbound

This paper cites Deep learning.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Deep learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:02.849558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 819204f0-ce43-4348-b8ed-c8db9917d6b4 · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.259290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6866dc2d-c93d-4872-8d74-cd6acdbec482 · outbound

This paper cites Learnable fourier features for multi-dimensional spatial positional encoding.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Learnable fourier features for multi-dimensional spatial positional encoding

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.206189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e88ee8a-25d4-465d-aa7e-78c1ce2597cb · outbound

This paper cites N-beats: Neural basis expansion analysis for interpretable time series forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting N-beats: Neural basis expansion analysis for interpretable time series forecasting

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.172471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1e6fbb45-05f6-4a84-99e6-3951c734949f · outbound

This paper cites Fc-gaga: Fully connected gated graph architecture for spatio-temporal traffic forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Fc-gaga: Fully connected gated graph architecture for spatio-temporal traffic forecasting

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.111338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2968de7c-f9d5-4098-a46d-9dce0edaea84 · outbound

This paper cites Satellite remote sensing for applied ecologists: opportunities and challenges.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Satellite remote sensing for applied ecologists: opportunities and challenges

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.059977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 98396b4c-9e29-406f-a7d1-9c458dd26260 · outbound

This paper cites Deep learning and process understanding for data-driven earth system science.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Deep learning and process understanding for data-driven earth system science

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:04.016062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f7392fec-8a8e-4230-af88-92076bb3dc72 · outbound

This paper cites Dynamic mode decomposition and its variants.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Dynamic mode decomposition and its variants

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.965496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 46c8ff9b-3a1d-43d8-b4d3-167f38743c97 · outbound

This paper cites Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.921593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 118ecd49-96a8-45b6-9cbe-e305cf14b6c3 · outbound

This paper cites Sequence to sequence learning with neural networks.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Sequence to sequence learning with neural networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:03.045946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22bfb4db-256c-4825-b32f-621ab4f25be5 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Fourier features let networks learn high frequency functions in low dimensional domains

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.871157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5c75d95-9a38-4602-aec3-4c51bcbcce04 · outbound

This paper cites Daymet: Monthly climate summaries on a 1-km grid for north america, version 4 r1.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Daymet: Monthly climate summaries on a 1-km grid for north america, version 4 r1

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.825517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ad02c98-ac48-4a38-90c3-818b29a44498 · outbound

This paper cites Attention is all you need.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Attention is all you need

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:03.113026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd8b964c-09bb-46c8-8472-53730df29e18 · outbound

This paper cites Short-term traffic forecasting: Where we are and where we’re going.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Short-term traffic forecasting: Where we are and where we’re going

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.769193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f325e583-ebe7-4cda-a650-3a486c1b4cfa · outbound

This paper cites Extracting dynamic mobility patterns by hankel dynamic modes decomposition.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Extracting dynamic mobility patterns by hankel dynamic modes decomposition

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.713894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ee7aded-9487-452a-b502-a9065ba79863 · outbound

This paper cites Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.678505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ce426b9c-6b2b-47f7-acb7-c9087fbd4e17 · outbound

This paper cites Graph wavenet for deep spatial-temporal graph modeling.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Graph wavenet for deep spatial-temporal graph modeling

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.638688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af3834b2-d2ab-42e5-944c-a471f830c9c6 · outbound

This paper cites Multi-scale context aggregation by dilated convolutions.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Multi-scale context aggregation by dilated convolutions

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.596980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 34628ccc-fe77-4526-ba81-0335c1360adc · outbound

This paper cites Temporal regularized matrix factorization for high-dimensional time series prediction.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Temporal regularized matrix factorization for high-dimensional time series prediction

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.549085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e90b775c-eaf8-4286-bb54-44aa30be2553 · outbound

This paper cites Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.502693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 59504331-e277-4ab7-979c-eece03c7d882 · outbound

This paper cites Gman: A graph multi-attention network for traffic prediction.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting Gman: A graph multi-attention network for traffic prediction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:03.461359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aa7958cd-e7a8-471a-9315-4dd6f84e54c3 · outbound

This paper cites write newline.

Dynamic Modes as Time Representation for Spatiotemporal Forecasting write newline

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:03.363540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 145dc751-e6f8-4e5e-b8df-88f380a5c625 · inbound

Frequency-Constrained Learning for Long-Term Forecasting cites this paper.

Frequency-Constrained Learning for Long-Term Forecasting Dynamic Modes as Time Representation for Spatiotemporal Forecasting

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T05:39:43.125770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.