Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Quantifying Interpretability and Trust in Machine Learning Systems
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1901.08558.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:16:01.096136Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T22:22:37.035444Z
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 03401215-6e96-4033-9dbd-9d7acac413cb · inbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change cites this paper.
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Quantifying Interpretability and Trust in Machine Learning Systems
Reference 191
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:16:01.096136Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d5f9603-ef84-4f17-bdca-a5c0a2d103f4 · inbound
Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features cites this paper.
Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Quantifying Interpretability and Trust in Machine Learning Systems
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:30:22.114835Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation af61755a-df38-4aca-acc4-448b3031a744 · inbound
Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds cites this paper.
Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds Quantifying Interpretability and Trust in Machine Learning Systems
Reference 9757
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T22:22:37.102372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.