Paper Citation Record · LEDGER

Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.10019.

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

pith.paper-citation-record.v1
2404.10019 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:27:34.948503Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3a2397c9-3ec1-4eac-ab8b-51ac6009d8fc · inbound

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes cites this paper.

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-16T10:27:34.948503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5c0f4e5-68fe-4cdb-86a4-ee92e3c05a2c · inbound

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference cites this paper.

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-22T02:50:57.865730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6edaff26-80f6-4e57-a095-208b8f12040b · inbound

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference cites this paper.

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-25T02:55:16.017508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 51bd9297-3ebd-4e27-8d68-fe509b5ace2e · inbound

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference cites this paper.

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:44:55.544623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.