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Paper Citation Record · LEDGER

Machine Learning meets the redshift evolution of the CMB Temperature

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2002.12700.

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

pith.paper-citation-record.v1
2002.12700 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:25:49.802516Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T13:05:35.121653Z

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 22d12575-a436-48be-97f8-bc6835e442bb · inbound

Revisiting the temperature evolution law of the CMB with gaussian processes cites this paper.

Revisiting the temperature evolution law of the CMB with gaussian processes Machine Learning meets the redshift evolution of the CMB Temperature

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T12:25:49.802516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:25:49.802516Z digest=sha256:0ff0f806ace1ff586f4619b2483eea8ff17f09e3c4ed3ac35501d5992b7e81e7

Observation 121e6fc1-8efa-40e0-becf-a727eb33d7c8 · inbound

Anisotropic cosmology using observational datasets: exploring via machine learning approaches cites this paper.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Machine Learning meets the redshift evolution of the CMB Temperature

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-06T13:05:35.233589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T13:05:29.899633Z digest=sha256:e1c345914e7269676d7337c1a06d0420ab1b5e9937f8377d1f8164ee7ad18b25

Observation 68fe3f09-f2d1-438a-9207-a91545671e8e · inbound

Investigating the cosmic distance duality relation with gamma-ray bursts cites this paper.

Investigating the cosmic distance duality relation with gamma-ray bursts Machine Learning meets the redshift evolution of the CMB Temperature

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-04T19:32:52.974807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T19:32:52.974807Z digest=sha256:1a21cda091d754569543191621dd392e204678d532a56d8c11277d199e963b5c