Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.20217.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-12T02:31:03.871783Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T13:38:19.669353Z
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 f6b9d5ce-1f45-4287-a6b2-9f36a16ad03f · inbound
Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing cites this paper.
Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T10:02:31.777195Z
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.
Observation 62fffc07-910a-4a51-a59d-b2622fbb2549 · inbound
Fine-tuning MLIP foundation models: strategies for accuracy and transferability cites this paper.
Fine-tuning MLIP foundation models: strategies for accuracy and transferability Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T13:38:19.670720Z
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.
Observation 23145205-e50e-419b-8c6d-dd5bbf9550d2 · inbound
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles cites this paper.
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T02:31:03.871783Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.