Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating Lyman-$\alpha$ Constraints for General Dark-Matter Velocity Distributions: Multiple Scales and Cautionary Tales
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2112.09105.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-02T10:27:10.986171Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T16:28:38.653563Z
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 c04f1ae7-fe1e-4012-8deb-7c42d309ccc6 · inbound
Seasons of Dark Matter Freeze-In Shaped by the Weather of the Early Universe cites this paper.
Seasons of Dark Matter Freeze-In Shaped by the Weather of the Early Universe Evaluating Lyman-$\alpha$ Constraints for General Dark-Matter Velocity Distributions: Multiple Scales and Cautionary Tales
Reference 54
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T23:30:29.606035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d427222c-a436-4c39-b11c-eddaa6f75059 · inbound
Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum cites this paper.
Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Evaluating Lyman-$\alpha$ Constraints for General Dark-Matter Velocity Distributions: Multiple Scales and Cautionary Tales
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T16:28:38.654838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 228359ea-e348-4549-9bcc-73bd970756cf · inbound
KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering cites this paper.
KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering Evaluating Lyman-$\alpha$ Constraints for General Dark-Matter Velocity Distributions: Multiple Scales and Cautionary Tales
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T10:36:51.970506Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.