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Paper Citation Record · LEDGER

Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.17812.

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

pith.paper-citation-record.v1
2406.17812 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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:11:57.837994Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T16:22:25.741721Z

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 a4813ff1-c2e9-4477-b1a9-ba44da1fb7d2 · inbound

Data Readiness for Scientific AI at Scale cites this paper.

Data Readiness for Scientific AI at Scale Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T11:11:57.837994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:11:57.837994Z digest=sha256:08302ed4ec452baee7c0eb4b95c58649d17ed2b417f412c7cd2212b4bc6c5878

Observation e0d5e685-6c9e-4a4e-81c4-aab92c0784d2 · inbound

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge cites this paper.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-05T16:22:25.747326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-05T16:22:25.387885Z digest=sha256:0f0ad44d7fac0a7e0134b307d9523048c53e8ce0dd799a34236a13e1289da839