Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:36:43.636079Z
Paper Citation Record · LEDGER
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops
As of 12 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.10228.
A citation records a reference. It does not transfer a finding from one paper to another.
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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
29 of 29 outbound references displayed
- verified exact1
- verified fuzzy17
- unresolved11
- parse uncertain0
- malformed identifier0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ef9207b9-3158-424b-87bd-8b5e7a676359 · outbound
This paper cites A time-dependent pa- rameter estimation framework for crop modeling.Scientific reports, 11(1):11437, 2021.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops A time-dependent pa- rameter estimation framework for crop modeling.Scientific reports, 11(1):11437, 2021
Reference 1
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:48.647773Z
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 ecd3005a-a8c3-4991-b775-ab819158c211 · outbound
This paper cites California agricultural statistics, 2024.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops California agricultural statistics, 2024
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:48.471789Z
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 6e4ac056-eeee-4fa6-bd81-9d13cae650ac · outbound
This paper cites High-resolution crop yield and water pro- ductivity dataset generated using random forest and remote sensing.Scientific data, 9(1):641, 2022.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops High-resolution crop yield and water pro- ductivity dataset generated using random forest and remote sensing.Scientific data, 9(1):641, 2022
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:48.306501Z
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 6be8ab21-df21-48ea-b339-845ec84115a1 · outbound
This paper cites Contribution of crop models to adaptation in wheat.Trends in plant science, 22(6):472–490, 2017.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Contribution of crop models to adaptation in wheat.Trends in plant science, 22(6):472–490, 2017
Reference 4
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:48.143656Z
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 851ce2a3-913a-4e41-a319-f6f7ac5ca440 · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.Inter- national Conference on Learning Representations (ICLR),.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops An image is worth 16x16 words: Transformers for image recognition at scale.Inter- national Conference on Learning Representations (ICLR),
Reference 5
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:47.963974Z
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 8c805b71-5517-4c5a-9946-80900b98fcf3 · outbound
This paper cites an unresolved cited work.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T04:36:47.846834Z
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 aa01ed6f-af72-41aa-87b0-7ff81f96d8b9 · outbound
This paper cites A gnn-rnn approach for harnessing geospa- tial and temporal information: application to crop yield pre- diction.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops A gnn-rnn approach for harnessing geospa- tial and temporal information: application to crop yield pre- diction
Reference 7
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:47.731310Z
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 d34438ad-b5b7-45d6-b4fb-62d4d962a69e · outbound
This paper cites Rice crop yield prediction using artificial neural networks.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Rice crop yield prediction using artificial neural networks
Reference 8
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:47.559466Z
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 47c1d3b3-ceeb-42b3-a71e-6b8e2fd04693 · outbound
This paper cites Panoptic segmentation of satellite image time series with convolu- tional temporal attention networks.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Panoptic segmentation of satellite image time series with convolu- tional temporal attention networks
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:47.401837Z
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 20101c54-a866-4f65-a051-5a4cff39efab · outbound
This paper cites Masked autoencoders are scalable vision learners.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Masked autoencoders are scalable vision learners
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:36:42.253571Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c78d768c-89f9-4584-85e0-6f3bedc4d24d · outbound
This paper cites To- ward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems sci- ence.Agricultural systems, 155:269–288, 2017.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops To- ward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems sci- ence.Agricultural systems, 155:269–288, 2017
Reference 11
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- raw_fallback, observed 2026-08-07T04:36:47.125021Z
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 43fe02b4-c229-4fc7-accc-d99d530a198c · outbound
This paper cites CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision Transformers.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision Transformers
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T04:36:43.869500Z
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 6998e48d-8cdf-4312-a034-436458443890 · outbound
This paper cites an unresolved cited work.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Unresolved cited work
Reference 13
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- unresolved
- raw_fallback, observed 2026-08-07T04:36:46.817047Z
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 16284b5a-2ba5-4e49-abde-fedff5ce453f · outbound
This paper cites Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning.Scientific Reports, 11(1):11132,.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning.Scientific Reports, 11(1):11132,
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:36:42.431700Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcd314bb-f40d-47d3-90a7-2113a708cfb4 · outbound
This paper cites Imagenet classification with deep convolutional neural net- works.Advances in neural information processing systems, 25, 2012.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Imagenet classification with deep convolutional neural net- works.Advances in neural information processing systems, 25, 2012
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:36:42.487233Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c878b26b-b768-4862-b74d-40f600f195af · outbound
This paper cites Mmst-vit: Climate change- aware crop yield prediction via multi-modal spatial-temporal vision transformer.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Mmst-vit: Climate change- aware crop yield prediction via multi-modal spatial-temporal vision transformer
Reference 16
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- raw_fallback, observed 2026-08-07T04:36:46.543589Z
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 229aefa9-97cf-48c5-9481-865ba33708cf · outbound
This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Swin transformer: Hierarchical vision transformer using shifted windows
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:36:42.666146Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 32658cad-e0ca-4506-bda8-101f516dba01 · outbound
This paper cites Openet: Filling a critical data gap in water management for the western united states.JAWRA Journal of the American Water Resources Association, 58(6):971–994, 2022.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Openet: Filling a critical data gap in water management for the western united states.JAWRA Journal of the American Water Resources Association, 58(6):971–994, 2022
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:46.317297Z
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 c14599ed-b81b-44f5-aa4c-b627d9399833 · outbound
This paper cites Advancing agricultural research using ma- chine learning algorithms.Scientific reports, 11(1):17879,.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Advancing agricultural research using ma- chine learning algorithms.Scientific reports, 11(1):17879,
Reference 19
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:46.176908Z
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 7b21efd6-b520-4610-8024-06110c691726 · outbound
This paper cites Department of Agriculture.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Department of Agriculture
Reference 20
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:45.974652Z
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 085f4ab3-6d6b-41dd-b585-2ee94d9b63a9 · outbound
This paper cites an unresolved cited work.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T04:36:45.881499Z
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 68469602-3205-4e3f-869e-dabeeff819b6 · outbound
This paper cites Daymet: Daily surface weather data on a 1-km grid for north america, version 2.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Daymet: Daily surface weather data on a 1-km grid for north america, version 2
Reference 22
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:45.716271Z
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 3f515319-4482-48b4-9204-671837b5fa80 · outbound
This paper cites Training data-efficient image transformers & distillation through at- tention.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Training data-efficient image transformers & distillation through at- tention
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:36:43.144398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 380bf8c7-cc3d-4a00-be35-51d05902d57b · outbound
This paper cites Department of Agriculture (USDA).
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Department of Agriculture (USDA)
Reference 24
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- verified fuzzy
- raw_fallback, observed 2026-08-07T04:36:44.953594Z
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 d6889f75-b520-4df7-8599-d7e5f265e14b · outbound
This paper cites USDA Na- tional Agricultural Statistics Service, 2020.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops USDA Na- tional Agricultural Statistics Service, 2020
Reference 25
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- raw_fallback, observed 2026-08-07T04:36:44.607305Z
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 32f58ec2-96a7-4114-a769-c0a1de0d9c90 · outbound
This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Attention is all you need.Advances in neural information processing systems, 30, 2017
Reference 26
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- no resolver link, observed 2026-08-07T04:36:43.355616Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d0fb6ae2-7d2d-495a-9336-0e81c43a7032 · outbound
This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Reference 27
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- raw_fallback, observed 2026-08-07T04:36:44.408883Z
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 a8b5aa94-1d25-4519-9e29-e020983ee524 · outbound
This paper cites an unresolved cited work.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Unresolved cited work
Reference 28
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- unresolved
- raw_fallback, observed 2026-08-07T04:36:44.210403Z
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 836ed537-2910-4745-8fab-5f666ad742b5 · outbound
This paper cites an unresolved cited work.
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops Unresolved cited work
Reference 29
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T04:36:44.041692Z
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
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