Paper Citation Record · LEDGER

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets

As of 7 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2607.23908.

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

pith.paper-citation-record.v1
2607.23908 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:35:25.333074Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

25 of 25 outbound references displayed

verified exact5
verified fuzzy0
unresolved19
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 49a3dd34-2f5e-41d4-9acd-9a31e064bb48 · outbound

This paper cites PLoS ONE18(7), e0287731 (2023).

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets PLoS ONE18(7), e0287731 (2023)

Reference 1

Resolution: verified exact; doi, observed 2026-07-31T23:35:54.080860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ba68ce0-9d6e-4c9e-abde-e28c647a2787 · outbound

This paper cites Geocarto International26(5), 341–358 (2011).https://doi.org/ 10.1080/10106049.2011.562309.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Geocarto International26(5), 341–358 (2011).https://doi.org/ 10.1080/10106049.2011.562309

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:22.164318Z

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Observation ea506c3c-c78c-4949-9b2a-7b65569b5f60 · outbound

This paper cites In: ACM SIGMOD International Conference on Management of Data.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets In: ACM SIGMOD International Conference on Management of Data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:22.268262Z

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Observation f6c6be96-ea34-47d2-8948-4b8795489ba5 · outbound

This paper cites AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:22.345633Z

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Observation 048604a1-9d45-4707-b131-d8509936b493 · outbound

This paper cites In: Proceedings of the TerraBytes ICML Workshop: Towards Global Datasets and Models for Earth Observation.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets In: Proceedings of the TerraBytes ICML Workshop: Towards Global Datasets and Models for Earth Observation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:22.449806Z

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Observation 1e3377e7-e285-4b96-9f69-c3cc3283489d · outbound

This paper cites In: Advances in Neural Information Processing Systems (NeurIPS) (2022).

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets In: Advances in Neural Information Processing Systems (NeurIPS) (2022)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:22.530169Z

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Observation 491cf6ca-5671-4707-ac74-95f27595259e · outbound

This paper cites Scientific Data7, 352 (2020).https://doi.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Scientific Data7, 352 (2020).https://doi

Reference 7

Resolution: verified exact; doi, observed 2026-07-31T23:35:53.966282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f28ed31c-9b69-413c-8013-086ff7895c65 · outbound

This paper cites Remote Sensing12(6), 1034 (2020).https: //doi.org/10.3390/rs12061034.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Remote Sensing12(6), 1034 (2020).https: //doi.org/10.3390/rs12061034

Reference 8

Resolution: verified exact; doi, observed 2026-07-31T23:35:53.857805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3bd932e9-1b8f-461a-93fa-381f6f3a34e4 · outbound

This paper cites Earth System Dynamics8(3), 677–696 (2017).

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Earth System Dynamics8(3), 677–696 (2017)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:22.999668Z

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Observation 3ac0e08d-290c-4b8c-a0e5-bb4f5fd649c1 · outbound

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Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:23.047326Z

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Observation 6dc2b091-2400-4298-b75e-e6d3574d6177 · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:23.186753Z

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Observation c01b58ac-c26a-44a3-8bc9-ce0e80e46e7f · outbound

This paper cites Journal of Experimental Social Psychology49(4), 764–766 (2013).https://doi.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Journal of Experimental Social Psychology49(4), 764–766 (2013).https://doi

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:23.365168Z

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Observation c4dd185c-aeb6-455e-8f28-17286eebbd45 · outbound

This paper cites In: IEEE International Con- ference on Data Mining (ICDM).

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets In: IEEE International Con- ference on Data Mining (ICDM)

Reference 13

Resolution: malformed identifier; no resolver link, observed 2026-07-31T23:35:23.475933Z

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Observation 5d83a93c-5050-4939-8dd4-ea1f257ef2ff · outbound

This paper cites Ecological Informatics p.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Ecological Informatics p

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:23.585298Z

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Observation 6a8489d5-c8b4-4b60-b390-ad831cc9f318 · outbound

This paper cites In: NeurIPS Datasets and Benchmarks Track (2021).

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets In: NeurIPS Datasets and Benchmarks Track (2021)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:23.691463Z

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Observation bc3ba999-88f9-4bc6-846c-56f52391f374 · outbound

This paper cites Journal of Artificial Intelligence Research70, 1373–1411 (2021).https://doi.org/10.1613/jair.1.12125.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Journal of Artificial Intelligence Research70, 1373–1411 (2021).https://doi.org/10.1613/jair.1.12125

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:23.846939Z

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Unavailable: canonical work link unavailable.

Observation 306b885f-e5f8-490a-84fe-d35d2fe03845 · outbound

This paper cites ACM Computing Surveys54(2), 1–38 (2021).https://doi.org/ 10.1145/3439950.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets ACM Computing Surveys54(2), 1–38 (2021).https://doi.org/ 10.1145/3439950

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:24.008179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adcea630-6d95-4901-80ab-90c8bf276373 · outbound

This paper cites Remote Sensing9(2), 173 (2017).https: //doi.org/10.3390/rs9020173.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Remote Sensing9(2), 173 (2017).https: //doi.org/10.3390/rs9020173

Reference 18

Resolution: verified exact; doi, observed 2026-07-31T23:35:53.748214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69f0cfee-66c5-44e9-b04c-609fd177dd47 · outbound

This paper cites John Wi- ley & Sons (1987).https://doi.org/10.1002/0471725382.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets John Wi- ley & Sons (1987).https://doi.org/10.1002/0471725382

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:24.327955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4067453a-ef3a-4b8d-951f-67f4c73ba7ce · outbound

This paper cites ISPRS Journal of Photogrammetry and Remote Sensing169, 421–435 (2020).https://doi.org/10.1016/j.isprsjprs.2020.06.006.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets ISPRS Journal of Photogrammetry and Remote Sensing169, 421–435 (2020).https://doi.org/10.1016/j.isprsjprs.2020.06.006

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:24.452514Z

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Unavailable: canonical work link unavailable.

Observation 2d2edddf-e5f5-46d0-9e0a-7f5d819a327b · outbound

This paper cites Nature Food4, 736–737 (2023).https://doi.org/ 10.1038/s43016-023-00841-7.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Nature Food4, 736–737 (2023).https://doi.org/ 10.1038/s43016-023-00841-7

Reference 21

Resolution: verified exact; doi, observed 2026-07-31T23:35:53.572578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation efd317cb-e363-4c9b-b04e-efaca8cb17cd · outbound

This paper cites IEEE Transactions on Neural Networks and Learn- ing Systems34(11), 8135–8153 (2022).https://doi.org/10.1109/TNNLS.2022.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets IEEE Transactions on Neural Networks and Learn- ing Systems34(11), 8135–8153 (2022).https://doi.org/10.1109/TNNLS.2022

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:24.780957Z

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Unavailable: canonical work link unavailable.

Observation ca3a2fd9-7177-4fdd-a491-3ccf63732ab1 · outbound

This paper cites Lightweight, Pre-trained Transformers for Remote Sensing Timeseries.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:24.993661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15dfcf01-cff3-478e-8d7c-cadc69460204 · outbound

This paper cites https://h3geo.org(2018) Embedding-based Outlier Detection for Crop Reference Data 17.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets https://h3geo.org(2018) Embedding-based Outlier Detection for Crop Reference Data 17

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:25.214976Z

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Observation 78f1305e-257d-48f7-9ba2-34a3d30227ed · outbound

This paper cites S1:Examples of outliers surfaced by the EBA detector.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets S1:Examples of outliers surfaced by the EBA detector

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:25.333074Z

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Pith citing papers

No inbound Pith citation observations are available.