Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262
As of 22 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1808.01614.
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 2026-07-20T06:30:07.809122+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T21:37:54.915455Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-02T19:07:18.505267Z
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 0b55c0f7-d7f1-48d9-b3c0-977dde4a30ac · inbound
RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification cites this paper.
RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T05:46:09.840690Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.
Observation c2a25050-5553-4024-9caf-b2dd578134fb · inbound
Re-imagining ISO 26262 in the Age of Autonomous Vehicles: Enhancing Controllability through Transferability and Predictability cites this paper.
Re-imagining ISO 26262 in the Age of Autonomous Vehicles: Enhancing Controllability through Transferability and Predictability Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T19:07:18.506440Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.