Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An interpretable machine learning framework for dark matter halo formation
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1906.06339.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:48:42.398430Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T11:06:53.072075Z
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 a00ffe78-5d92-4595-9af1-7a44f2b65cd3 · inbound
AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution cites this paper.
AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution An interpretable machine learning framework for dark matter halo formation
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:48:42.398430Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3446f8f-e2ea-40a3-8bc2-a90466a6cd20 · inbound
Segmenting proto-halos with vision transformers cites this paper.
Segmenting proto-halos with vision transformers An interpretable machine learning framework for dark matter halo formation
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T01:46:58.007779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2e4b72e0-5bf3-445b-8885-9409fddc396b · inbound
Full Nonlinear Velocity Reconstruction With Transformer and Ensemble Tree Machine Learning cites this paper.
Full Nonlinear Velocity Reconstruction With Transformer and Ensemble Tree Machine Learning An interpretable machine learning framework for dark matter halo formation
Reference 79
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T11:06:53.073443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.