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Paper Citation Record · LEDGER

Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.07043.

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

pith.paper-citation-record.v1
2111.07043 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T05:25:47.083510Z

measured 1 of 1 external citation measurements

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

Source: pith, 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

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a7da96e4-b17d-47e5-a057-1d2c0faff1c9 · inbound

Cluster-Weighted Training of Deep Surrogate Models for Subgrid Turbulent Transport cites this paper.

Cluster-Weighted Training of Deep Surrogate Models for Subgrid Turbulent Transport Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-07-10T15:27:19.976371Z

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.

source=pdf_text observed=2026-07-10T15:20:03.307437Z digest=sha256:c8b0d5a0d63c2728b888649af64c22bb02823f11414202746caa955b10298666

Observation b55edfe6-03b4-498d-a985-055d63595a0c · inbound

Developing Machine Learning Models of Subgrid Turbulent Transport for Quiet Sun 3D Radiative Hydrodynamic Simulations cites this paper.

Developing Machine Learning Models of Subgrid Turbulent Transport for Quiet Sun 3D Radiative Hydrodynamic Simulations Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms

Reference 24

Resolution
unresolved
no resolver link, observed 2026-07-13T05:25:47.083510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T05:25:47.083510Z digest=sha256:a2523cc782739125e0046646d2f8e0b71a7e90495049851175859810e4ee90f5