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Paper Citation Record · LEDGER

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.02748.

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

pith.paper-citation-record.v1
2502.02748 v4

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 2026-08-03T06:30:56.289259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:23:13.643808Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-11T18:15:01.373222Z

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 39f9046f-7051-4dea-9b6d-c5fa079d31a1 · inbound

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python cites this paper.

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

Reference 286

Resolution
metadata mismatch
local_arxiv, observed 2026-07-11T16:18:08.062622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-07-11T16:17:26.963678Z digest=sha256:f2fc653ffd8b1148d66bb3c95949eaa01fc81c4e56d75b44a642e6d75a168276

Observation 27e4db33-803f-4d82-86e5-7ed633a803c1 · inbound

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks cites this paper.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-01T22:23:13.643808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:23:13.643808Z digest=sha256:2595c1819955e6dea31ec3ab60943b2c16fbb5321ae02a51799e49a2bfcb1438