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Paper Citation Record · LEDGER

Deep modeling of quasar variability

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2003.01241.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2003.01241 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:34.536362Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T21:45:35.396949Z

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 ece85d11-6700-4d94-bfb0-90a0ab65a8ba · inbound

Continuum optical-UV and X-ray variability of AGN: current results and future challenges cites this paper.

Continuum optical-UV and X-ray variability of AGN: current results and future challenges Deep modeling of quasar variability

Reference 287

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:45:35.401597Z

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.

source=arxiv_source observed=2026-08-06T21:45:34.536362Z digest=sha256:a4272938ef7ce5c5bbe5e61743e5ef057eb9559d0781db7c96ed59a558032422

Observation aa4c7ffe-e275-4d81-8d29-6aa8b385afab · inbound

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features cites this paper.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Deep modeling of quasar variability

Reference 127

Resolution
unresolved
no resolver link, observed 2026-08-01T21:02:29.447731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T21:02:29.447731Z digest=sha256:c75d7b68ed5e450ed85e25bc6f2af540f111fd0c07527a384d5dbaa18ebfb447