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Paper Citation Record · LEDGER

Novel deep learning methods for track reconstruction

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.06111.

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

pith.paper-citation-record.v1
1810.06111 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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:05:33.456502Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:16:16.768632Z

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 69ec49da-df7f-4ae8-b87d-6edf60d0daaf · inbound

Noise Filtering Algorithm Based on Graph Neural Network for STCF Drift Chamber cites this paper.

Noise Filtering Algorithm Based on Graph Neural Network for STCF Drift Chamber Novel deep learning methods for track reconstruction

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T18:05:33.456502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:05:33.456502Z digest=sha256:866a5ff2857f18c682a9df05e876549d697990530e6f5787f40729cc2392de33

Observation f9537d58-9a4b-4611-972d-c93b8c0cabfa · inbound

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures cites this paper.

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures Novel deep learning methods for track reconstruction

Reference 296

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:14:26.441470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T22:14:26.387647Z digest=sha256:d5d2db2dba08a8b4d4eb7e3b2653b90c39171c008cf2174ea5218b88e8dee854