Paper Citation Record · LEDGER

DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1903.02557.

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

pith.paper-citation-record.v1
1903.02557 v3

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 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:20:58.127908Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:22:47.263367Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 65fd5e38-0db7-4f0b-ace7-c7f738995803 · inbound

How to Find Variable Active Galactic Nuclei with Machine Learning cites this paper.

How to Find Variable Active Galactic Nuclei with Machine Learning DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T12:20:58.127908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d6eae86-9d6d-47b3-a899-85e477165535 · inbound

Cosmological parameter estimation from large-scale structure deep learning cites this paper.

Cosmological parameter estimation from large-scale structure deep learning DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-14T10:47:27.837081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da1c66cd-57eb-43fa-bd0e-237d4dab305a · 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 DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.410108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 442a3250-1692-4278-b6cd-a1c78f1e5b17 · inbound

From stellar light to astrophysical insight: automating variable star research with machine learning cites this paper.

From stellar light to astrophysical insight: automating variable star research with machine learning DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

Reference 129

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:22:47.370349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.