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Paper Citation Record · LEDGER

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting

As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2608.06082.

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

pith.paper-citation-record.v1
2608.06082 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:19:03.863484Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

24 of 24 outbound references displayed

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External citation measurements

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Outbound references

Observation ee75be40-a642-4b83-8f28-5eb9362befac · outbound

This paper cites It is the primary pathway for water returning to the atmosphere, influencing freshwater availability, agricultural productivity and ecological balance.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting It is the primary pathway for water returning to the atmosphere, influencing freshwater availability, agricultural productivity and ecological balance

Reference 1

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Observation 4288a6ea-ff61-4f28-b92f-55653d6da8f0 · outbound

This paper cites An overview of pertinent literature that relates to our study is given below.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting An overview of pertinent literature that relates to our study is given below

Reference 2

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Observation dd8b1f93-fd3a-46b1-9655-471b426b7765 · outbound

This paper cites This diverse area is situated between longitudes of 87° E and 98° E, and latitudes of 21° N to 30° N.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting This diverse area is situated between longitudes of 87° E and 98° E, and latitudes of 21° N to 30° N

Reference 3

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Observation d2188f59-1e93-416c-8768-36bd75a64dee · outbound

This paper cites Evaluated performance metrics of the model has been shown in Table 3.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Evaluated performance metrics of the model has been shown in Table 3

Reference 4

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Observation 62b76ffb-b026-4ef6-9fc5-26f3fa92f718 · outbound

This paper cites Here, we employed the transformer-based attention mechanism to leverage the efficiency of a CNN -based encoder-decoder model.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Here, we employed the transformer-based attention mechanism to leverage the efficiency of a CNN -based encoder-decoder model

Reference 5

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Observation 9165a483-339c-4b54-a9fe-10b67f8d3519 · outbound

This paper cites Deep learning in environmental remote sensing: Achievements and challenges.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Deep learning in environmental remote sensing: Achievements and challenges

Reference 6

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Observation 24d1b221-6b6c-4fea-889a-ce1fedf1d9b0 · outbound

This paper cites To be Artificial Intelligence for sustainability or not to be sustainable Artificial Intelligence.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting To be Artificial Intelligence for sustainability or not to be sustainable Artificial Intelligence

Reference 7

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Observation d8ed1082-7fc1-4910-90ad-1ea7eb9750ae · outbound

This paper cites SmaAt-UNet: Precipitation nowcasting using a small attention -UNet architecture.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting SmaAt-UNet: Precipitation nowcasting using a small attention -UNet architecture

Reference 8

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Observation 783e6733-7a7f-44c1-803e-4c69778eaf3b · outbound

This paper cites Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data

Reference 9

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Observation 903ced0a-f01e-4a6b-b644-2b88bff958d3 · outbound

This paper cites trajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting trajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning

Reference 10

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Observation 34268935-3a7f-4828-a0de-2065efe885a4 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

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Observation 95834f33-e1fa-4be0-9e80-4b52a883f6b7 · outbound

This paper cites Densely connected convolutional networks.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Densely connected convolutional networks

Reference 12

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Observation 8f94f499-872f-4304-9885-32f1f5ce3504 · outbound

This paper cites DeePS at: A deep learning model for prediction of satellite images for nowcasting purposes.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting DeePS at: A deep learning model for prediction of satellite images for nowcasting purposes

Reference 13

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Observation 3864a570-13ee-412f-88b2-31b6325188b4 · outbound

This paper cites Precipitation Prediction Using an Ensemble of Lightweight Learners.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Precipitation Prediction Using an Ensemble of Lightweight Learners

Reference 14

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local_arxiv, observed 2026-08-07T15:19:04.004271Z

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Observation 37e83169-8016-4723-b695-7cbabe6cb0e2 · outbound

This paper cites EfficientRainNet: Leveraging EfficientNetV2 for memory -efficient rainfall nowcasting.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting EfficientRainNet: Leveraging EfficientNetV2 for memory -efficient rainfall nowcasting

Reference 15

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raw_fallback, observed 2026-08-07T15:19:05.550856Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation def0b0a6-5260-4540-aa6f-2047e39b3819 · outbound

This paper cites Lightweight residual U -Net model for hourly precipitation nowcasting.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Lightweight residual U -Net model for hourly precipitation nowcasting

Reference 16

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Observation 5336e16e-baeb-4d18-9fe5-53230e00b420 · outbound

This paper cites Onset of summer monsoon in Northeast India is preceded by enhanced transpiration.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Onset of summer monsoon in Northeast India is preceded by enhanced transpiration

Reference 17

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Observation 898bacec-e0c2-4b10-9f5e-5e1743af4211 · outbound

This paper cites Climate change impacts on socio -hydrological spaces of the Brahmaputra floodplain in Assam, Northeast India: A review.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Climate change impacts on socio -hydrological spaces of the Brahmaputra floodplain in Assam, Northeast India: A review

Reference 18

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Observation 1c1029da-0b56-4901-a0c5-67b149ec7578 · outbound

This paper cites Optimal rainfall threshold for monsoon rice production in India varies across space and time.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Optimal rainfall threshold for monsoon rice production in India varies across space and time

Reference 19

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Observation 6aa1ecd6-c590-422b-aa9f-13b68cc06571 · outbound

This paper cites Persistent loss of biologically -rich tropical forests in the Indian Eastern Himalaya.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Persistent loss of biologically -rich tropical forests in the Indian Eastern Himalaya

Reference 20

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Observation bba0777c-3d31-44bd-a42f-1c537f7350d4 · outbound

This paper cites NASA global precipitation measurement (GPM) integrated multi -satellite retrievals for GPM (IMERG).

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting NASA global precipitation measurement (GPM) integrated multi -satellite retrievals for GPM (IMERG)

Reference 21

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Observation e10f5fbe-c417-4eff-8632-0b6e1c22a7c1 · outbound

This paper cites Attention is all you need.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Attention is all you need

Reference 22

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Observation e878bd9f-e58a-44ab-b1b4-d32921e032b7 · outbound

This paper cites Precipitation nowcasting using transformer -based generative models and transfer learning for improved disaster preparedness.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Precipitation nowcasting using transformer -based generative models and transfer learning for improved disaster preparedness

Reference 23

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Observation c543b376-8285-4c78-b8dc-98df0eb9347c · outbound

This paper cites Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling.

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling

Reference 24

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