Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An approach to cold dark matter deviation and the $H_{0}$ tension problem by using machine learning
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2104.01077.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:30:02.824469Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T16:45:51.356021Z
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 e7ed889c-4146-4ce2-a90a-17dc633ef7ef · inbound
New cosmological constraints on the evolution of dark matter energy density cites this paper.
New cosmological constraints on the evolution of dark matter energy density An approach to cold dark matter deviation and the $H_{0}$ tension problem by using machine learning
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:30:02.824469Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a62bd067-8d4e-470a-9546-3b3d6324f58e · inbound
Bayesian and Machine-Learning Analyses of Nonminimal $f(Q)$ Gravity and $H_0$ Tension cites this paper.
Bayesian and Machine-Learning Analyses of Nonminimal $f(Q)$ Gravity and $H_0$ Tension An approach to cold dark matter deviation and the $H_{0}$ tension problem by using machine learning
Reference 73
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T23:23:59.800480Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6fa862f0-7277-4986-ab20-ee67a34dd4a1 · inbound
Redshift evolution of the Hubble constant: Constraints and new insights from an interacting dark energy model cites this paper.
Redshift evolution of the Hubble constant: Constraints and new insights from an interacting dark energy model An approach to cold dark matter deviation and the $H_{0}$ tension problem by using machine learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T19:30:17.420932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 22299152-11fb-4a3f-b2d4-9a6049d0126d · inbound
Unifying Early and Late Dark Energy: Dynamical Requirements and Obstructions cites this paper.
Unifying Early and Late Dark Energy: Dynamical Requirements and Obstructions An approach to cold dark matter deviation and the $H_{0}$ tension problem by using machine learning
Reference 177
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T16:45:51.357441Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.