Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Shapley explainability on the data manifold
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2006.01272.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T05:05:23.217096Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T16:06:14.555851Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation e783e80c-75e1-421f-9236-64b698da5860 · inbound
ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things cites this paper.
ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things Shapley explainability on the data manifold
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:52:02.325185Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0bce2024-7f83-4844-8853-5a4a33d36b9f · inbound
Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery cites this paper.
Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery Shapley explainability on the data manifold
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:21:30.172361Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28070c0e-c9f8-453e-9822-c86f5b06c55d · inbound
Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments cites this paper.
Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Shapley explainability on the data manifold
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T08:46:26.665058Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73542e21-7a71-4b4e-86cd-ef78c07c00d5 · inbound
From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows cites this paper.
From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows Shapley explainability on the data manifold
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T16:06:14.559336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7798c051-96b2-4540-a828-76c9fe46b141 · inbound
Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System cites this paper.
Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System Shapley explainability on the data manifold
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T05:05:23.217096Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.