Paper Citation Record · LEDGER

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2608.07406.

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

pith.paper-citation-record.v1
2608.07406 v1

Coverage vector

measured 42 of 42 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 86850867-02fc-44a8-84c7-4a7724d62b56 · outbound

This paper cites AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers

Reference 1

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This paper cites Applications of Explainable artificial intelligence in Earth system science.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Applications of Explainable artificial intelligence in Earth system science

Reference 5

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This paper cites an unresolved cited work.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 12

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This paper cites Shapley explainability on the data manifold.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Shapley explainability on the data manifold

Reference 18

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This paper cites Ham, Y.-G., J.-H.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Ham, Y.-G., J.-H

Reference 19

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This paper cites Counet, F.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Counet, F

Reference 27

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This paper cites T., and G.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System T., and G

Reference 30

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Observation cf061c90-27c1-4bbf-a0fe-99d079ea8c62 · outbound

This paper cites Bocquet, L.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Bocquet, L

Reference 32

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This paper cites Investigating the influence of noise and distractors on the interpretation of neural networks.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Investigating the influence of noise and distractors on the interpretation of neural networks

Reference 34

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This paper cites Schiller, J., S.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Schiller, J., S

Reference 35

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This paper cites Cimorelli, G.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Cimorelli, G

Reference 37

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This paper cites Taly, and Q.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Taly, and Q

Reference 38

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System 64 Spuler, F

Reference 41

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This paper cites ClimaX: A foundation model for weather and climate.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System ClimaX: A foundation model for weather and climate

Reference 58

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This paper cites Vieira Passos, G.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Vieira Passos, G

Reference 63

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Observation b799f8a9-f116-4b5c-8058-c9f02210a609 · outbound

This paper cites Geophysical Research Letters,51 (12), e2023GL107.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Geophysical Research Letters,51 (12), e2023GL107

Reference 74

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This paper cites Zhou, L., and R.-H.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Zhou, L., and R.-H

Reference 82

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 83

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 89

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 92

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 93

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This paper cites Schroeter, S.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Schroeter, S

Reference 140

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 169

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This paper cites FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale

Reference 232

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Chassagnon, M

Reference 259

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Talib, F

Reference 261

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This paper cites Official Journal of the European Union, https:// artificialintelligenceact.eu/.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Official Journal of the European Union, https:// artificialintelligenceact.eu/

Reference 295

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 368

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Laloyaux, 2020: Machine learning for model error inference and correction

Reference 377

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 405

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 453

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Shazeer, N

Reference 465

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Barriopedro, J

Reference 540

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This paper cites Binder, G.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Binder, G

Reference 560

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This paper cites A., and Coauthors, 2019: Current status of Landsat program, science, and applications.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System A., and Coauthors, 2019: Current status of Landsat program, science, and applications

Reference 650

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System C., and R

Reference 701

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Bengio, A

Reference 869

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 949

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Observation 24b639e8-3e06-4c6b-9672-df4bc57f47f0 · outbound

This paper cites Moser, M.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Moser, M

Reference 1230

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Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Unresolved cited work

Reference 3557

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Observation 6405e32c-985f-49e2-b2d2-994063cce653 · outbound

This paper cites Efficient Constrained Pattern Mining Using Dynamic Item Ordering for Explainable Classification.

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Efficient Constrained Pattern Mining Using Dynamic Item Ordering for Explainable Classification

Reference 3952

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Pith citing papers

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