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  1. Home
  2. Signal Canvas
  3. WED-Net: A Weather-Effect Disentanglement Network with Causa
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WED-Net: A Weather-Effect Disentanglement Network with Causal Augmentation for Urban Flow Prediction

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0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: WED-Net: A Weather-Effect Disentanglement Network with Causal Augmentation for Urban Flow Prediction

PDF: https://arxiv.org/pdf/2601.22586v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Paper Mode

WED-Net: A Weather-Effect Disentanglement Network with Causal Augmentation for Urban Flow Prediction

Overall score: 7/10
Lineage: 8320e4675296…
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

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Dimensions overall score 7.0

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Keep exploring

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Prior Work
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NeuroDDAF: Neural Dynamic Diffusion-Advection Fields with Evidential Fusion for Air Quality Forecasting
Score 7.0stable
Prior Work
AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting
Score 7.0stable
Prior Work
Anchored-Branched Steady-state WInd Flow Transformer (AB-SWIFT): a metamodel for 3D atmospheric flow in urban environments
Score 7.0stable
Higher Viability
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Score 9.0up

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Related Resources

  • How does AI contribute to the development of urban air mobility?(question)

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