Paper Citation Record · LEDGER

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization

As of 13 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2411.16728.

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pith.paper-citation-record.v1
2411.16728 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T16:38:36.296404Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 32c67a25-efb6-4193-9624-fef574f51d59 · outbound

This paper cites https://damo.alibaba.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization https://damo.alibaba

Reference 1

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Observation b860bef0-b53e-4e39-ad07-25d02cec7feb · outbound

This paper cites Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks

Reference 2

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Observation f20d92b4-0501-4151-9202-cb1496196d87 · outbound

This paper cites The quiet revolution of numerical weather prediction.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The quiet revolution of numerical weather prediction

Reference 3

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Observation d1ea305c-5317-4190-9886-fbc7be0205e8 · outbound

This paper cites Curriculum learning.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Curriculum learning

Reference 4

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Observation b4c3b1be-f6c4-4b19-88ed-3d5e93cc8aef · outbound

This paper cites Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast

Reference 5

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Observation e93b0115-8929-49cb-8dc1-b7592350e1ec · outbound

This paper cites A Foundation Model for the Earth System.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization A Foundation Model for the Earth System

Reference 6

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Observation 4036ca90-5280-478b-923b-d1f6a9a06d38 · outbound

This paper cites Spherical fourier neural operators: Learning stable dynamics on the sphere.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Spherical fourier neural operators: Learning stable dynamics on the sphere

Reference 7

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Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Unresolved cited work

Reference 8

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Observation d03454f7-fdc9-4c00-a260-78c50b7ad154 · outbound

This paper cites Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead

Reference 9

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Observation 1ee465b0-ffcc-49df-9f22-d42bd4934553 · outbound

This paper cites Fuxi: a cascade machine learning forecasting system for 15-day global weather fore- cast.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Fuxi: a cascade machine learning forecasting system for 15-day global weather fore- cast

Reference 10

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Source-reported events for the cited work

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Observation 5b619cb9-d9ad-4e26-b81e-f75d8c55b60b · outbound

This paper cites FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models

Reference 11

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Observation 2cec2625-1d3c-49fb-a9ae-780f31d655b1 · outbound

This paper cites Fundamentals of numerical weather predic- tion.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Fundamentals of numerical weather predic- tion

Reference 12

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Observation 56ebdfdb-b27f-4131-90cf-29c74957b122 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation ba20c02b-cefe-4e4e-8b8e-edd6d61f203f · outbound

This paper cites Siamese masked autoencoders.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Siamese masked autoencoders

Reference 14

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Observation d66c4078-79e6-42f0-9fbb-9a49c61706ef · outbound

This paper cites The era5 global reanalysis.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The era5 global reanalysis

Reference 15

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Observation 23eeb563-7563-46a4-88b8-0e0234ace5c6 · outbound

This paper cites Generalized Teacher Forcing for Learning Chaotic Dynamics.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Generalized Teacher Forcing for Learning Chaotic Dynamics

Reference 16

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Observation 4c9ddab9-6a85-4c61-9b1b-36f88850dcde · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Parameter-efficient transfer learning for nlp

Reference 17

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Observation f4e93099-d36c-440a-8631-5d35ede0003d · outbound

This paper cites The Platonic Representation Hypothesis.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The Platonic Representation Hypothesis

Reference 18

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Observation c62a4d72-c77e-4053-aa63-69f93e7b60d5 · outbound

This paper cites Auto-Encoding Variational Bayes.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Auto-Encoding Variational Bayes

Reference 19

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Observation ce2fc628-240e-4259-8aef-269b59755a09 · outbound

This paper cites Similarity of neural network represen- tations revisited.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Similarity of neural network represen- tations revisited

Reference 20

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Observation e9366d4e-6b98-4bc4-bd48-fd3f2926fad8 · outbound

This paper cites Learning skillful medium-range global weather forecasting.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Learning skillful medium-range global weather forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.315186Z

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Observation 8813b66d-3595-4826-a264-b404c7abe0da · outbound

This paper cites Analysis methods for numerical weather prediction.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Analysis methods for numerical weather prediction

Reference 22

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation db216036-faff-4730-a193-3566c123a433 · outbound

This paper cites Deterministic nonperiodic flow.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Deterministic nonperiodic flow

Reference 23

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Observation 15e061a0-8a56-43a3-bc32-86f10a6d4c8e · outbound

This paper cites On the difficulty of learning chaotic dynamics with rnns.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization On the difficulty of learning chaotic dynamics with rnns

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.263719Z

Source-reported events for the cited work

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Observation 0a4f412e-9483-44a4-8641-6b8adf1efc0b · outbound

This paper cites Adaptive bias correction for im- proved subseasonal forecasting.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Adaptive bias correction for im- proved subseasonal forecasting

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.247864Z

Source-reported events for the cited work

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Observation fb9bfb56-139d-455c-a957-b6877c56146d · outbound

This paper cites ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction

Reference 26

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Observation be229320-21df-4ae5-a3b8-9e6c9fbe4e95 · outbound

This paper cites Gupta, and Aditya Grover.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Gupta, and Aditya Grover

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.231178Z

Source-reported events for the cited work

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Observation 93bfc50c-8462-4597-a75a-17d596a5cbbe · outbound

This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 28

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Observation a0323edf-4e5e-43f7-81bb-0480ef99d660 · outbound

This paper cites Pendergrass, Gerald A.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Pendergrass, Gerald A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.215725Z

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Observation 319382a1-4840-4446-bae7-94ca80c36b16 · outbound

This paper cites The role of model and initial condition error in numerical weather forecasting in- vestigated with an observing system simulation experiment.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The role of model and initial condition error in numerical weather forecasting in- vestigated with an observing system simulation experiment

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.199058Z

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Observation d226d039-3f16-42f7-88ba-35717d0de333 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization U- net: Convolutional networks for biomedical image segmen- tation

Reference 31

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Observation 80b2f9b4-68dd-4994-9ad5-d177e9e519d3 · outbound

This paper cites Learning representations by back-propagating er- rors.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Learning representations by back-propagating er- rors

Reference 32

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Observation 43e43798-6292-4db4-90df-3aec434f9157 · outbound

This paper cites The ncep climate forecast system version 2.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The ncep climate forecast system version 2

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.163219Z

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Observation fb2a5149-deba-454e-b200-effb97e1a840 · outbound

This paper cites Lamb, Yu Huang, and Pierre Gen- tine.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Lamb, Yu Huang, and Pierre Gen- tine

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.148388Z

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Observation 0b39a050-b04d-4f01-8bbe-0eeb94791315 · outbound

This paper cites ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs

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Observation 6b877285-c4b7-4eb5-9479-3199aeaefd5f · outbound

This paper cites Evolution of ecmwf sub-seasonal forecast skill scores.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Evolution of ecmwf sub-seasonal forecast skill scores

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.132655Z

Source-reported events for the cited work

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Observation ed2d29a7-3646-471f-8d2b-4163c9880958 · outbound

This paper cites The sub-seasonal to seasonal prediction project (s2s) and the prediction of ex- treme events.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The sub-seasonal to seasonal prediction project (s2s) and the prediction of ex- treme events

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.116089Z

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 de527eb8-0589-4c49-954e-c549896f07ed · outbound

This paper cites Subseasonal to seasonal prediction project: Bridging the gap between weather and climate.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Subseasonal to seasonal prediction project: Bridging the gap between weather and climate

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.100492Z

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 7c22f492-4e01-4350-a5a9-16dbc5917466 · outbound

This paper cites Backpropagation through time: what it does and how to do it.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Backpropagation through time: what it does and how to do it

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.084886Z

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 b5cd732c-b008-470a-847f-6f610a6d7b2b · outbound

This paper cites An all-season real-time multivariate mjo index: Development of an in- dex for monitoring and prediction.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization An all-season real-time multivariate mjo index: Development of an in- dex for monitoring and prediction

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.068369Z

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 9c34418d-447f-4168-8be5-3d8c67156268 · outbound

This paper cites Potential applications of subseasonal-to-seasonal (s2s) predictions.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Potential applications of subseasonal-to-seasonal (s2s) predictions

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.050648Z

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 1abead12-1fa4-4445-b35c-d9906da4fe76 · outbound

This paper cites The met office global coupled model 2.0 (gc2) con- figuration.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The met office global coupled model 2.0 (gc2) con- figuration

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.032690Z

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 35215364-290b-444e-b2d9-6fa4355853a0 · outbound

This paper cites The beijing climate center climate system model (bcc-csm): The main progress from cmip5 to cmip6.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization The beijing climate center climate system model (bcc-csm): The main progress from cmip5 to cmip6

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.016881Z

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 fc037171-6cb4-47d6-adc3-87032beeee59 · outbound

This paper cites Estimating the uncertainty in a regional climate model related to initial and lateral boundary conditions.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Estimating the uncertainty in a regional climate model related to initial and lateral boundary conditions

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:22.000737Z

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 a81e0b13-afa0-46aa-aeed-f02d48fb4ef2 · outbound

This paper cites Vargas Zeppetello, David S.

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization Vargas Zeppetello, David S

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:21.984982Z

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 98ef0209-7184-48c2-825e-53672569f936 · outbound

This paper cites Gradient descent with identity initialization efficiently learns positive definite linear trans- formations by deep residual networks.

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:19:21.968608Z

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.

Pith citing papers

Observation 575e9154-0cc1-4e95-881a-fe0ce2aa8d66 · inbound

Prediction of Drought and Flash Drought in Africa at the Seasonal-to-Subseasonal Scale using the Community Research Earth Digital Intelligence Twin Framework cites this paper.

Prediction of Drought and Flash Drought in Africa at the Seasonal-to-Subseasonal Scale using the Community Research Earth Digital Intelligence Twin Framework Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization

Reference 171

Resolution: verified exact; arxiv_id, observed 2026-05-08T16:53:29.703567Z

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.