Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1907.05764.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:31:03.578825Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T19:46:13.806524Z
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 b666a72f-0dba-489e-94e2-54aff64dd86f · inbound
Emulating Recombination with Neural Networks using Universal Differential Equations cites this paper.
Emulating Recombination with Neural Networks using Universal Differential Equations CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:31:03.578825Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aea6f07b-c696-4e54-932f-4eb625d62bd5 · inbound
Effort: a fast and differentiable emulator for the Effective Field Theory of the Large Scale Structure of the Universe cites this paper.
Effort: a fast and differentiable emulator for the Effective Field Theory of the Large Scale Structure of the Universe CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:31:06.058803Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5a1befa9-d28e-43a7-b172-2f82811d475b · inbound
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective cites this paper.
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers
Reference 264
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:46:13.810376Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.